Fly_diversity_analysis_aug2025
Eddy Dowle
2025-08-15
Fly diversity
I’ve done quite a few different things, just trying to work out the best way to use it. I would say that it seems fairly consistent in the patterns between my ‘all in analysis’ and ‘only day 1’ analysis. Which I think is good. Apple, pear and kiwifruit suffer a bit later on when it comes to having samples with only 0’s for non bee species or only one species which makes MDS plots etc imposible to converge so they are filtered in and out throughout the analysis.
##############
#data sorting#
##############
#basic diversity stats + MDS
#means I need to deal with the naming issues on species ID
#sorting data feel free to ignore its just getting it into a format I can use
#setwd("C:/Users/hrlexd/Dropbox/PlantAndFood (1)/B4BI/Review_paper2024")
apple<-read_excel('C:/Users/hrlexd/Dropbox/PlantAndFood (1)/B4BI/Review_paper2024/Daily insect counts all crops for Eddy_mod.xlsx',sheet='Apple')
pear<-read_excel('C:/Users/hrlexd/Dropbox/PlantAndFood (1)/B4BI/Review_paper2024/Daily insect counts all crops for Eddy_mod.xlsx',sheet='Pear')
avocado<-read_excel('C:/Users/hrlexd/Dropbox/PlantAndFood (1)/B4BI/Review_paper2024/Daily insect counts all crops for Eddy_mod.xlsx',sheet='Avocado')
kiwifruit<-read_excel('C:/Users/hrlexd/Dropbox/PlantAndFood (1)/B4BI/Review_paper2024/Daily insect counts all crops for Eddy_mod.xlsx',sheet='Kiwifruit')
pakchoi<-read_excel('C:/Users/hrlexd/Dropbox/PlantAndFood (1)/B4BI/Review_paper2024/Daily insect counts all crops for Eddy_mod.xlsx',sheet='Pak choi')
radish<-read_excel('C:/Users/hrlexd/Dropbox/PlantAndFood (1)/B4BI/Review_paper2024/Daily insect counts all crops for Eddy_mod.xlsx',sheet='Radish')
onion<-read_excel('C:/Users/hrlexd/Dropbox/PlantAndFood (1)/B4BI/Review_paper2024/Daily insect counts all crops for Eddy_mod.xlsx',sheet='Onion')
carrot<-read_excel('C:/Users/hrlexd/Dropbox/PlantAndFood (1)/B4BI/Review_paper2024/Daily insect counts all crops for Eddy_mod.xlsx',sheet='Carrot')
#brad wants them grouped on the column labeled 'Ordering'
#scientific name is wacky so stick to the ordering column as the grouping variable
#remove “Ephydridae spp.” from Pak choi and onion data
#as per brad is wasnt counted in the other crops so needs to be removed
onion<-onion %>% filter(`Scientific name`!='Ephydridae spp.')
pakchoi<-pakchoi %>% filter(`Scientific name`!='Ephydridae spp.')
################
#data wrangling#
################
#sorting out daily counts as diversity statistics will be based on daily counts:
###apple
#day 1, 2, 3
apple_day1<-apple %>% select(-`Day 2`,-`Day 3`) %>% mutate(Day='Day_1') %>% filter(`Day 1`>0) %>% rename(raw_count = `Day 1`)
apple_day2<-apple %>% select(-`Day 1`,-`Day 3`) %>% mutate(Day='Day_2') %>% filter(`Day 2`>0) %>% rename(raw_count = `Day 2`)
apple_day3<-apple %>% select(-`Day 1`,-`Day 2`) %>% mutate(Day='Day_3') %>% filter(`Day 3`>0) %>% rename(raw_count = `Day 3`)
apple_day<-rbind(apple_day1,apple_day2,apple_day3)
#just make sure everything is tidy tidy
apple_fly<-apple_day%>% filter(Grouping!='Bees')%>% mutate(Ordering = factor(Ordering)) %>% mutate(sample_ID = paste0('Apple_',Region,'_',`Location and season`,'_',Day)) %>% select(sample_ID,Ordering,Region,`Location and season`,Day,raw_count,Crop) %>% group_by(sample_ID,Ordering,Region,`Location and season`,Day,Crop) %>% summarise(raw_count=sum(raw_count)) %>% ungroup()## `summarise()` has grouped output by 'sample_ID', 'Ordering', 'Region',
## 'Location and season', 'Day'. You can override using the `.groups` argument.#some replicates have only 0 counts for non bees and are being dropped so need to be added back in
samples<-apple_day%>% mutate(Ordering = factor(Ordering)) %>% mutate(sample_ID = paste0('Apple_',Region,'_',`Location and season`,'_',Day)) %>% select(sample_ID,Region,`Location and season`,Day,Crop) %>% unique()
apple_fly<-full_join(apple_fly,samples)## Joining with `by = join_by(sample_ID, Region, `Location and season`, Day,
## Crop)`###pear
#day 1, 2, 3
pear_day1<-pear %>% select(-`Day 2`,-`Day 3`) %>% mutate(Day='Day_1') %>% filter(`Day 1`>0) %>% rename(raw_count = `Day 1`)
pear_day2<-pear %>% select(-`Day 1`,-`Day 3`) %>% mutate(Day='Day_2') %>% filter(`Day 2`>0) %>% rename(raw_count = `Day 2`)
pear_day3<-pear %>% select(-`Day 1`,-`Day 2`) %>% mutate(Day='Day_3') %>% filter(`Day 3`>0) %>% rename(raw_count = `Day 3`)
pear_day<-rbind(pear_day1,pear_day2,pear_day3)
#just make sure everything is tidy tidy
pear_fly<-pear_day%>% filter(Grouping!='Bees')%>% mutate(Ordering = factor(Ordering)) %>% mutate(sample_ID = paste0('Pear_',Region,'_',`Location and season`,'_',Day)) %>% select(sample_ID,Ordering,Region,`Location and season`,Day,raw_count,Crop) %>% group_by(sample_ID,Ordering,Region,`Location and season`,Day,Crop) %>% summarise(raw_count=sum(raw_count)) %>% ungroup()## `summarise()` has grouped output by 'sample_ID', 'Ordering', 'Region',
## 'Location and season', 'Day'. You can override using the `.groups` argument.#some replicates have only 0 counts for non bees and are being dropped so need to be added back in
samples<-pear_day%>% mutate(Ordering = factor(Ordering)) %>% mutate(sample_ID = paste0('Pear_',Region,'_',`Location and season`,'_',Day)) %>% select(sample_ID,Region,`Location and season`,Day,Crop) %>% unique()
pear_fly<-full_join(pear_fly,samples)## Joining with `by = join_by(sample_ID, Region, `Location and season`, Day,
## Crop)`###avocado
#day 1, 2, 3
avocado_day1<-avocado %>% select(-`Day 2`,-`Day 3`) %>% mutate(Day='Day_1') %>% filter(`Day 1`>0) %>% rename(raw_count = `Day 1`)
avocado_day2<-avocado %>% select(-`Day 1`,-`Day 3`) %>% mutate(Day='Day_2') %>% filter(`Day 2`>0) %>% rename(raw_count = `Day 2`)
avocado_day3<-avocado %>% select(-`Day 1`,-`Day 2`) %>% mutate(Day='Day_3') %>% filter(`Day 3`>0) %>% rename(raw_count = `Day 3`)
avocado_day<-rbind(avocado_day1,avocado_day2,avocado_day3)
#just make sure everything is tidy tidy
avocado_fly<-avocado_day%>% filter(Grouping!='Bees')%>% mutate(Ordering = factor(Ordering)) %>% mutate(sample_ID = paste0('Avocado_',Region,'_',`Location and season`,'_',Day)) %>% select(sample_ID,Ordering,Region,`Location and season`,Day,raw_count,Crop) %>% group_by(sample_ID,Ordering,Region,`Location and season`,Day,Crop) %>% summarise(raw_count=sum(raw_count)) %>% ungroup()## `summarise()` has grouped output by 'sample_ID', 'Ordering', 'Region',
## 'Location and season', 'Day'. You can override using the `.groups` argument.#some replicates have only 0 counts for non bees and are being dropped so need to be added back in
samples<-avocado_day%>% mutate(Ordering = factor(Ordering)) %>% mutate(sample_ID = paste0('Avocado_',Region,'_',`Location and season`,'_',Day)) %>% select(sample_ID,Region,`Location and season`,Day,Crop) %>% unique()
avocado_fly<-full_join(avocado_fly,samples)## Joining with `by = join_by(sample_ID, Region, `Location and season`, Day,
## Crop)`###kiwifruit
#day 1, 2, 3
kiwifruit_day1<-kiwifruit %>% select(-`Day 2`,-`Day 3`) %>% mutate(Day='Day_1') %>% filter(`Day 1`>0) %>% rename(raw_count = `Day 1`)
kiwifruit_day2<-kiwifruit %>% select(-`Day 1`,-`Day 3`) %>% mutate(Day='Day_2') %>% filter(`Day 2`>0) %>% rename(raw_count = `Day 2`)
kiwifruit_day3<-kiwifruit %>% select(-`Day 1`,-`Day 2`) %>% mutate(Day='Day_3') %>% filter(`Day 3`>0) %>% rename(raw_count = `Day 3`)
kiwifruit_day<-rbind(kiwifruit_day1,kiwifruit_day2,kiwifruit_day3)
#just make sure everything is tidy tidy
kiwifruit_fly<-kiwifruit_day%>% filter(Grouping!='Bees')%>% mutate(Ordering = factor(Ordering)) %>% mutate(sample_ID = paste0('Kiwifruit_',Region,'_',`Location and season`,'_',Day)) %>% select(Crop,sample_ID,Ordering,Region,`Location and season`,Day,raw_count) %>% group_by(sample_ID,Ordering,Region,`Location and season`,Day,Crop) %>% summarise(raw_count=sum(raw_count)) %>% ungroup()## `summarise()` has grouped output by 'sample_ID', 'Ordering', 'Region',
## 'Location and season', 'Day'. You can override using the `.groups` argument.#some replicates have only 0 counts for non bees and are being dropped so need to be added back in
samples<-kiwifruit_day%>% mutate(Ordering = factor(Ordering)) %>% mutate(sample_ID = paste0('Kiwifruit_',Region,'_',`Location and season`,'_',Day)) %>% select(sample_ID,Region,`Location and season`,Day,Crop) %>% unique()
kiwifruit_fly<-full_join(kiwifruit_fly,samples)## Joining with `by = join_by(sample_ID, Region, `Location and season`, Day,
## Crop)`###pakchoi
#day 1
pakchoi_day1<-pakchoi %>% mutate(Day='Day_1') %>% rename(raw_count = `Corrected Daily Count`)
pakchoi_day<-pakchoi_day1
#just make sure everything is tidy tidy
pakchoi_fly<-pakchoi_day%>% filter(Grouping!='Bees')%>% mutate(Ordering = factor(Ordering)) %>% mutate(sample_ID = paste0('Pakchoi_',Region,'_',`Location and season`,'_',Day)) %>% select(sample_ID,Ordering,Region,`Location and season`,Day,raw_count,Crop) %>% group_by(sample_ID,Ordering,Region,`Location and season`,Day,Crop) %>% summarise(raw_count=sum(raw_count)) %>% ungroup()## `summarise()` has grouped output by 'sample_ID', 'Ordering', 'Region',
## 'Location and season', 'Day'. You can override using the `.groups` argument.#some replicates have only 0 counts for non bees and are being dropped so need to be added back in
samples<-pakchoi_day%>% mutate(Ordering = factor(Ordering)) %>% mutate(sample_ID = paste0('Pakchoi_',Region,'_',`Location and season`,'_',Day)) %>% select(sample_ID,Region,`Location and season`,Day,Crop) %>% unique()
pakchoi_fly<-full_join(pakchoi_fly,samples)## Joining with `by = join_by(sample_ID, Region, `Location and season`, Day,
## Crop)`###radish
#day 1
radish_day1<-radish %>% mutate(Day='Day_1') %>% rename(raw_count = `Corrected Daily Count`)
radish_day<-radish_day1
#just make sure everything is tidy tidy
radish_fly<-radish_day%>% filter(Grouping!='Bees')%>% mutate(Ordering = factor(Ordering)) %>% mutate(sample_ID = paste0('Radish_',Region,'_',`Location and season`,'_',Day)) %>% select(sample_ID,Ordering,Region,`Location and season`,Day,raw_count,Crop) %>% group_by(sample_ID,Ordering,Region,`Location and season`,Day,Crop) %>% summarise(raw_count=sum(raw_count)) %>% ungroup()## `summarise()` has grouped output by 'sample_ID', 'Ordering', 'Region',
## 'Location and season', 'Day'. You can override using the `.groups` argument.#some replicates have only 0 counts for non bees and are being dropped so need to be added back in
samples<-radish_day%>% mutate(Ordering = factor(Ordering)) %>% mutate(sample_ID = paste0('Radish_',Region,'_',`Location and season`,'_',Day)) %>% select(sample_ID,Region,`Location and season`,Day,Crop) %>% unique()
radish_fly<-full_join(radish_fly,samples)## Joining with `by = join_by(sample_ID, Region, `Location and season`, Day,
## Crop)`###onion
#day 1
onion_day1<-onion %>% mutate(Day='Day_1') %>% rename(raw_count = `Corrected Daily Count`)
onion_day<-onion_day1
#just make sure everything is tidy tidy
onion_fly<-onion_day%>% filter(Grouping!='Bees')%>% mutate(Ordering = factor(Ordering)) %>% mutate(sample_ID = paste0('Onion_',Region,'_',`Location and season`,'_',Day)) %>% select(sample_ID,Ordering,Region,`Location and season`,Day,raw_count,Crop) %>% group_by(sample_ID,Ordering,Region,`Location and season`,Day,Crop) %>% summarise(raw_count=sum(raw_count)) %>% ungroup()## `summarise()` has grouped output by 'sample_ID', 'Ordering', 'Region',
## 'Location and season', 'Day'. You can override using the `.groups` argument.#some replicates have only 0 counts for non bees and are being dropped so need to be added back in
samples<-onion_day%>% mutate(Ordering = factor(Ordering)) %>% mutate(sample_ID = paste0('Onion_',Region,'_',`Location and season`,'_',Day)) %>% select(sample_ID,Region,`Location and season`,Day,Crop) %>% unique()
onion_fly<-full_join(onion_fly,samples)## Joining with `by = join_by(sample_ID, Region, `Location and season`, Day,
## Crop)`###carrot
#day 1 2 3
carrot_day1<-carrot %>% select(-`Day 2`,-`Day 3`) %>% mutate(Day='Day_1') %>% filter(`Day 1`>0) %>% rename(raw_count = `Day 1`)
carrot_day2<-carrot %>% select(-`Day 1`,-`Day 3`) %>% mutate(Day='Day_2') %>% filter(`Day 2`>0) %>% rename(raw_count = `Day 2`)
carrot_day3<-carrot %>% select(-`Day 1`,-`Day 2`) %>% mutate(Day='Day_3') %>% filter(`Day 3`>0) %>% rename(raw_count = `Day 3`)
carrot_day<-rbind(carrot_day1,carrot_day2,carrot_day3)
#just make sure everything is tidy tidy
carrot_fly<-carrot_day%>% filter(Grouping!='Bees')%>% mutate(Ordering = factor(Ordering)) %>% mutate(sample_ID = paste0('Carrot_',Region,'_',`Location and season`,'_',Day)) %>% select(sample_ID,Ordering,Region,`Location and season`,Day,raw_count,Crop) %>% group_by(sample_ID,Ordering,Region,`Location and season`,Day,Crop) %>% summarise(raw_count=sum(raw_count)) %>% ungroup()## `summarise()` has grouped output by 'sample_ID', 'Ordering', 'Region',
## 'Location and season', 'Day'. You can override using the `.groups` argument.#some replicates have only 0 counts for non bees and are being dropped so need to be added back in
samples<-carrot_day%>% mutate(Ordering = factor(Ordering)) %>% mutate(sample_ID = paste0('Carrot_',Region,'_',`Location and season`,'_',Day)) %>% select(sample_ID,Region,`Location and season`,Day,Crop) %>% unique()
carrot_fly<-full_join(carrot_fly,samples)## Joining with `by = join_by(sample_ID, Region, `Location and season`, Day,
## Crop)`##############################
#generating tables and counts#
##############################
allcrops_per_r_fly<-rbind(apple_fly,pear_fly,avocado_fly,kiwifruit_fly,pakchoi_fly,radish_fly,onion_fly,carrot_fly)
unique(allcrops_per_r_fly$Crop)## [1] "Apple" "Pear" "Avocado" "Kiwifruit" "Pak Choi" "Radish"
## [7] "Onion" "Carrot"counts<-allcrops_per_r_fly %>% ungroup() %>% select(sample_ID,Ordering,raw_count)
#I need to fill up the NA's for the samples with 0 flies with some dummy 0 for ones species to keep them in the next stage - so this is just to add a 0 value in and I've chosed the insect '27' at random (cause i know it occurs in the dataset so Im not adding something not present)
#in '' as its a factor not a number
counts$Ordering[is.na(counts$Ordering)] <- '27'
counts$raw_count[is.na(counts$raw_count)] <- 0
#transform wide
counts_wide<-counts %>% pivot_wider(names_from=`Ordering`,values_from = `raw_count`)
counts_wide[is.na(counts_wide)] <- 0
counts_wide<-counts_wide %>% remove_rownames %>% column_to_rownames(var="sample_ID")
#setting up meta data sheet for anova against variables, species richness vs crops
meta_data<-allcrops_per_r_fly %>% ungroup() %>% select(sample_ID,Crop,Region,`Location and season`,Day) %>% unique()
#adding in: spring vs summer flowering to metadata table
head(meta_data)## # A tibble: 6 × 5
## sample_ID Crop Region `Location and season` Day
## <chr> <chr> <chr> <chr> <chr>
## 1 Apple_Hawkes Bay_104 Moteo-Pa road _… Apple Hawke… 104 Moteo-Pa road _2… Day_1
## 2 Apple_Hawkes Bay_104 Moteo-Pa road _… Apple Hawke… 104 Moteo-Pa road _2… Day_3
## 3 Apple_Hawkes Bay_185 Lawn road, Hast… Apple Hawke… 185 Lawn road, Hasti… Day_1
## 4 Apple_Hawkes Bay_185 Lawn road, Hast… Apple Hawke… 185 Lawn road, Hasti… Day_2
## 5 Apple_Hawkes Bay_185 Lawn road, Hast… Apple Hawke… 185 Lawn road, Hasti… Day_3
## 6 Apple_Hawkes Bay_2080 Pakawhai Road_… Apple Hawke… 2080 Pakawhai Road_2… Day_1meta_data<-meta_data %>% mutate(flowertime = case_when(Crop == 'Apple' ~ "Spring",Crop == 'Pear' ~ "Spring",Crop == 'Avocado' ~ "Spring",Crop == 'Kiwifruit' ~ "Spring",Crop == 'Carrot' ~ "Summer",Crop == 'Radish' ~ "Summer",Crop == 'Onion' ~ "Summer",Crop == 'Pak Choi' ~ "Summer"))
#regrouping regions
meta_data$Region_original<-meta_data$Region
meta_data<-meta_data %>% mutate(Region = case_when(Region_original == 'Ashburton' ~ "Canterbury", Region_original == 'Darfield-Kirwee' ~ "Canterbury",Region_original == 'North Waimakariri' ~ "Canterbury",Region_original == 'Leeston' ~ "Canterbury",Region_original == 'Methven' ~ "Canterbury",Region_original == 'Waipara' ~ "Canterbury",Region_original == 'Kaitaia' ~ "Northland",Region_original == 'Whangarei' ~ "Whangarei",Region_original == 'Wairarapa' ~ "Wairarapa",Region_original == 'Bay of Plenty' ~ "Bay of Plenty",Region_original == 'Hawkes Bay' ~ "Hawkes Bay",Region_original == 'Motueka' ~ "Tasman",Region_original == 'Nelson' ~ "Tasman",Region_original == 'Takaka' ~ "Tasman",Region_original == 'Marlborough' ~ "Marlborough",Region_original == 'Gisborne' ~ "Poverty Bay",Region_original == 'Central Otago' ~ "Otago",Region_original == 'Southland' ~ "Southland",Region_original == 'Auckland' ~ "Auckland",Region_original == 'Canterbury' ~ "Canterbury"))
#unique(meta_data$Region)
#meta_data %>% select(Region, Region_original) %>% unique()Network plots
These are based on average counts per day across all replicates
#need to calculate average number of counts per tree per sample
#head(allcrops_per_r_fly)
allcrops_summarisedcounts<-allcrops_per_r_fly %>% select(sample_ID,Ordering,Crop,raw_count) %>% group_by(Crop,Ordering) %>% summarise(total_insects=sum(raw_count))## `summarise()` has grouped output by 'Crop'. You can override using the
## `.groups` argument.sample_count<-allcrops_per_r_fly %>% select(sample_ID,Crop) %>% group_by(Crop) %>% summarise(sample_per_crop=n_distinct(sample_ID))
allcrops_summarisedcounts<-left_join(allcrops_summarisedcounts,sample_count) %>% mutate(average_count=total_insects/sample_per_crop)## Joining with `by = join_by(Crop)`#remove those NAs that were added in for the 0's
allcrops_summarisedcounts <- na.omit(allcrops_summarisedcounts)
brads_col_2<-read_excel('C:/Users/hrlexd/Dropbox/PlantAndFood (1)/B4BI/Review_paper2024/Copy of fly Pie chart data for Eddyv2_flies.xlsx',sheet='Insect_ordering')
head(brads_col_2)## # A tibble: 6 × 3
## Code Species Colour
## <dbl> <chr> <chr>
## 1 1 Apis mellifera gray94
## 2 2 Bombus terrestris gray85
## 3 3 Bombus ruderatus/hortorum gray85
## 4 4 Megachile rotundata gray85
## 5 5 Lasioglossum sordidum gray85
## 6 6 Leioproctus spp. gray85brads_col_2$color_hex<-col2hex(brads_col_2$Colour)
nodes_notthebees<-data.frame(name=c(as.character(allcrops_summarisedcounts$Ordering),as.character((allcrops_summarisedcounts$Crop))) %>% unique())
allcrops_summarisedcounts$IDsource <- match(allcrops_summarisedcounts$Ordering, nodes_notthebees$name)-1
allcrops_summarisedcounts$IDtarget <- match(allcrops_summarisedcounts$Crop, nodes_notthebees$name)-1
node_col<-nodes_notthebees
brads_col_2$Code<-as.character(brads_col_2$Code)
brads_col_2_cut <-brads_col_2 %>% select(Code,color_hex) %>% unique() %>% na.omit()
node_col<-left_join(node_col,brads_col_2_cut,by=c('name'='Code'))
#add colours for crops
col_bound<-rcartocolor::carto_pal(n=9,name='Prism')
node_col<-node_col %>% mutate(color_hex = case_when(name == 'Apple' ~ "#5F4690",name == 'Avocado' ~ "#1D6996",name == 'Carrot' ~ "#38A6A5",name == 'Kiwifruit' ~ "#0F8554",name == 'Onion' ~ "#73AF48",name == 'Pak Choi' ~ "#EDAD08",name == 'Pear' ~ "#E17C05",name == 'Radish' ~ "#CC503E",
TRUE ~ color_hex))
#brads new colours
node_col<-node_col %>% mutate(color_hex = case_when(name == 'Apple' ~ "#007b83",name == 'Avocado' ~ "#6e7d3c",name == 'Carrot' ~ "#CD951C",name == 'Kiwifruit' ~ "#b5a53c",name == 'Onion' ~ "#6baed6",name == 'Pak Choi' ~ "#E0C591",name == 'Pear' ~ "#b78080",name == 'Radish' ~ "#516295",
TRUE ~ color_hex))
#col_bound<-c("#6e7d3c", "#007b83","#b78080","#b5a53c","#E0C591","#516295","#CD951C","#b4daeb")
allcrops_summarisedcounts<-left_join(allcrops_summarisedcounts,node_col,by=c('Ordering'='name'))
plot_ly(
type = "sankey",
orientation = "h",
# valueformat=".2f", #switch this on if doing per 6 trees otherwise it will rescale decimals
node = list(pad = 15,
thickness = 20,
line = list(color = "black", width = 0.5),
label = node_col$name,
color=node_col$color_hex
),
link = list(source = allcrops_summarisedcounts$IDsource,
target = allcrops_summarisedcounts$IDtarget,
value = allcrops_summarisedcounts$average_count,
color=allcrops_summarisedcounts$color_hex),
textfont = list(size = 10),
width = 700,
height = 500
) %>%
layout(title = "Sankey Diagram",
font = list(size = 14),
margin = list(t = 40, l = 20, r = 10, b = 10))#o set opacity I need to switch to rgba format rather than hex
allcrops_summarisedcounts<-allcrops_summarisedcounts %>% rowwise() %>% mutate(rgba=paste0('rgba(',paste(col2rgb(color_hex),collapse=','),',0.5)'))
plot_ly(
type = "sankey",
arrangement='snap',
#domain =list(
# x=c(0,1),
# y=c(0,1)
#),
orientation = "h",
# valueformat=".2f", #switch this on if doing per 6 trees otherwise it will rescale decimals
node = list(pad = 15,
thickness = 15,
line = list(color = "black", width = 0.5),
label = as.factor(node_col$name),
color=node_col$color_hex
),
link = list(source = allcrops_summarisedcounts$IDsource,
target = allcrops_summarisedcounts$IDtarget,
value = allcrops_summarisedcounts$average_count,
# value = test_merge_ave6trees$sum_6_tree),
color=allcrops_summarisedcounts$rgba
)
) %>%
layout(title = "Sankey Diagram",
font = list(size = 10),
xaxis = list(showgrid=F,zeroline=F),
yaxis=list(showgrid=F,zeroline=F))#brad wants crops at the top (just to be difficult)
allcrops_summarisedcounts<-allcrops_per_r_fly %>% select(sample_ID,Ordering,Crop,raw_count) %>% group_by(Crop,Ordering) %>% summarise(total_insects=sum(raw_count))## `summarise()` has grouped output by 'Crop'. You can override using the
## `.groups` argument.sample_count<-allcrops_per_r_fly %>% select(sample_ID,Crop) %>% group_by(Crop) %>% summarise(sample_per_crop=n_distinct(sample_ID))
allcrops_summarisedcounts<-left_join(allcrops_summarisedcounts,sample_count) %>% mutate(average_count=total_insects/sample_per_crop)## Joining with `by = join_by(Crop)`#remove those NAs that were added in for the 0's
allcrops_summarisedcounts <- na.omit(allcrops_summarisedcounts)
brads_col_2<-read_excel('C:/Users/hrlexd/Dropbox/PlantAndFood (1)/B4BI/Review_paper2024/Copy of fly Pie chart data for Eddyv2_flies.xlsx',sheet='Insect_ordering')
head(brads_col_2)## # A tibble: 6 × 3
## Code Species Colour
## <dbl> <chr> <chr>
## 1 1 Apis mellifera gray94
## 2 2 Bombus terrestris gray85
## 3 3 Bombus ruderatus/hortorum gray85
## 4 4 Megachile rotundata gray85
## 5 5 Lasioglossum sordidum gray85
## 6 6 Leioproctus spp. gray85brads_col_2$color_hex<-col2hex(brads_col_2$Colour)
nodes_notthebees<-data.frame(name=c(as.character(allcrops_summarisedcounts$Ordering),as.character((allcrops_summarisedcounts$Crop))) %>% unique())
allcrops_summarisedcounts$IDsource <- match(allcrops_summarisedcounts$Crop, nodes_notthebees$name)-1
allcrops_summarisedcounts$IDtarget <- match(allcrops_summarisedcounts$Ordering, nodes_notthebees$name)-1
node_col<-nodes_notthebees
brads_col_2$Code<-as.character(brads_col_2$Code)
brads_col_2_cut <-brads_col_2 %>% select(Code,color_hex) %>% unique() %>% na.omit()
node_col<-left_join(node_col,brads_col_2_cut,by=c('name'='Code'))
#add colours for crops
col_bound<-rcartocolor::carto_pal(n=9,name='Prism')
node_col<-node_col %>% mutate(color_hex = case_when(name == 'Apple' ~ "#007b83",name == 'Avocado' ~ "#6e7d3c",name == 'Carrot' ~ "#CD951C",name == 'Kiwifruit' ~ "#b5a53c",name == 'Onion' ~ "#6baed6",name == 'Pak Choi' ~ "#E0C591",name == 'Pear' ~ "#b78080",name == 'Radish' ~ "#516295",
TRUE ~ color_hex))
allcrops_summarisedcounts<-left_join(allcrops_summarisedcounts,node_col,by=c('Ordering'='name'))
plot_ly(
type = "sankey",
orientation = "v",
# valueformat=".2f", #switch this on if doing per 6 trees otherwise it will rescale decimals
node = list(pad = 15,
thickness = 20,
line = list(color = "black", width = 0.5),
label = node_col$name,
color=node_col$color_hex
),
link = list(source = allcrops_summarisedcounts$IDsource,
target = allcrops_summarisedcounts$IDtarget,
value = allcrops_summarisedcounts$average_count,
color=allcrops_summarisedcounts$color_hex),
textfont = list(size = 10),
width = 700,
height = 500
) %>%
layout(font = list(size = 14),
margin = list(t = 40, l = 20, r = 10, b = 10))#o set opacity I need to switch to rgba format rather than hex
allcrops_summarisedcounts<-allcrops_summarisedcounts %>% rowwise() %>% mutate(rgba=paste0('rgba(',paste(col2rgb(color_hex),collapse=','),',0.5)'))
plot_ly(
type = "sankey",
arrangement='snap',
#domain =list(
# x=c(0,1),
# y=c(0,1)
#),
orientation = "v",
# valueformat=".2f", #switch this on if doing per 6 trees otherwise it will rescale decimals
node = list(pad = 15,
thickness = 15,
line = list(color = "black", width = 0.5),
label = as.factor(node_col$name),
color=node_col$color_hex
),
link = list(source = allcrops_summarisedcounts$IDsource,
target = allcrops_summarisedcounts$IDtarget,
value = allcrops_summarisedcounts$average_count,
# value = test_merge_ave6trees$sum_6_tree),
color=allcrops_summarisedcounts$rgba
)
) %>%
layout(font = list(size = 10),
xaxis = list(showgrid=F,zeroline=F),
yaxis=list(showgrid=F,zeroline=F))#talked about changing the colours schemes to things just done for SVD as well
#or only the top 10 count sets
#or intersect of both
#top 10 count sets
head(allcrops_summarisedcounts)## # A tibble: 6 × 9
## # Rowwise: Crop
## Crop Ordering total_insects sample_per_crop average_count IDsource IDtarget
## <chr> <chr> <dbl> <int> <dbl> <dbl> <dbl>
## 1 Apple 9 1 67 0.0149 67 0
## 2 Apple 13 6 67 0.0896 67 1
## 3 Apple 14 49 67 0.731 67 2
## 4 Apple 16 5 67 0.0746 67 3
## 5 Apple 23 3 67 0.0448 67 4
## 6 Apple 24 2 67 0.0299 67 5
## # ℹ 2 more variables: color_hex <chr>, rgba <chr>top_10_taxa<-allcrops_summarisedcounts %>% select(Ordering, average_count) %>% group_by(Ordering) %>% summarise(totalsum=sum(average_count)) %>% arrange(desc(totalsum)) %>% slice_head(n = 10)## Adding missing grouping variables: `Crop`unique(node_col$name)## [1] "9" "13" "14" "16" "23" "24"
## [7] "25" "32" "33" "34" "38" "51"
## [13] "52" "54" "55" "56" "65" "11"
## [19] "15" "22" "26" "40" "47" "10"
## [25] "12" "17" "18" "19" "21" "27"
## [31] "31" "36" "37" "41" "42" "43"
## [37] "44" "46" "48" "57" "61" "63"
## [43] "67" "73" "74" "76" "29" "30"
## [49] "53" "58" "66" "72" "62" "71"
## [55] "49" "50" "39" "28" "70" "77"
## [61] "59" "60" "68" "69" "75" "78"
## [67] "20" "Apple" "Avocado" "Carrot" "Kiwifruit" "Onion"
## [73] "Pak Choi" "Pear" "Radish"top_10_taxa<-c(top_10_taxa$Ordering,"Apple","Avocado","Carrot","Kiwifruit","Onion","Pak Choi","Pear","Radish")
#taxa that have SVD data associated with them are:
#calliphora stygia: 13
#calliphora vicina: 14
#lucilla sericata: 15
#pollenia psudorudis: 19
#pollenia spp.: 20
#hydrotaea rostrata: 22
#delia platura: 24 #bit of a shit pollinator in terms of SVD
#oxysarcodexia varia: 26
#prophystricia alcis: 27
#eristalis tenax: 32
#melangyna novaezelandiae: 33
#melanostoma fasciatum: 34
#helophilus hochstelleri: 36
#helophilus cingulatus: 37
#allographta dorsalis: 39
#odontomyia spp: 43
#dilophus nigrostigma: 47
#zorion guttigerum: 55 #bit of a shit pollinator in terms of SVD
svd_taxa<-c('13','14','15','19','20','22','24','26','27','32','33','34','36','37','39','43','47','55',"Apple","Avocado","Carrot","Kiwifruit","Onion","Pak Choi","Pear","Radish")
#top 10
node_col<-node_col %>% mutate(col_top10=case_when(name %in% top_10_taxa ~ color_hex, TRUE ~ '#D3D3D3'))
allcrops_summarisedcounts<-allcrops_summarisedcounts %>% mutate(col_top10=case_when(Ordering %in% top_10_taxa ~ color_hex, TRUE ~ '#D3D3D3'))
plot_ly(
type = "sankey",
orientation = "v",
# valueformat=".2f", #switch this on if doing per 6 trees otherwise it will rescale decimals
node = list(pad = 15,
thickness = 20,
line = list(color = "black", width = 0.5),
label = node_col$name,
color=node_col$col_top10
),
link = list(source = allcrops_summarisedcounts$IDsource,
target = allcrops_summarisedcounts$IDtarget,
value = allcrops_summarisedcounts$average_count,
color=allcrops_summarisedcounts$col_top10),
textfont = list(size = 10),
width = 700,
height = 500
) %>%
layout(font = list(size = 14),
margin = list(t = 40, l = 20, r = 10, b = 10))#to set grey opacity I need to switch to rgba format rather than hex
allcrops_summarisedcounts<-allcrops_summarisedcounts %>% rowwise() %>% mutate(rgba_top10=paste0('rgba(',paste(col2rgb(col_top10),collapse=','),',1)'))
allcrops_summarisedcounts$rgba_top10<-gsub('rgba(211,211,211,1)','rgba(211,211,211,0.5)',allcrops_summarisedcounts$rgba_top10, fixed = TRUE)
plot_ly(
type = "sankey",
arrangement='snap',
#domain =list(
# x=c(0,1),
# y=c(0,1)
#),
orientation = "v",
# valueformat=".2f", #switch this on if doing per 6 trees otherwise it will rescale decimals
node = list(pad = 15,
thickness = 15,
line = list(color = "black", width = 0.5),
label = as.factor(node_col$name),
color=node_col$col_top10
),
link = list(source = allcrops_summarisedcounts$IDsource,
target = allcrops_summarisedcounts$IDtarget,
value = allcrops_summarisedcounts$average_count,
# value = test_merge_ave6trees$sum_6_tree),
color=allcrops_summarisedcounts$rgba_top10
)
) %>%
layout(font = list(size = 10),
xaxis = list(showgrid=F,zeroline=F),
yaxis=list(showgrid=F,zeroline=F))#things that we have SVD for:
#svd
node_col<-node_col %>% mutate(col_svd=case_when(name %in% svd_taxa ~ color_hex, TRUE ~ '#D3D3D3'))
allcrops_summarisedcounts<-allcrops_summarisedcounts %>% mutate(col_svd=case_when(Ordering %in% svd_taxa ~ color_hex, TRUE ~ '#D3D3D3'))
plot_ly(
type = "sankey",
orientation = "v",
# valueformat=".2f", #switch this on if doing per 6 trees otherwise it will rescale decimals
node = list(pad = 15,
thickness = 20,
line = list(color = "black", width = 0.5),
label = node_col$name,
color=node_col$col_svd
),
link = list(source = allcrops_summarisedcounts$IDsource,
target = allcrops_summarisedcounts$IDtarget,
value = allcrops_summarisedcounts$average_count,
color=allcrops_summarisedcounts$col_svd),
textfont = list(size = 10),
width = 700,
height = 500
) %>%
layout(font = list(size = 14),
margin = list(t = 40, l = 20, r = 10, b = 10))#to set grey opacity I need to switch to rgba format rather than hex
allcrops_summarisedcounts<-allcrops_summarisedcounts %>% rowwise() %>% mutate(rgba_svd=paste0('rgba(',paste(col2rgb(col_svd),collapse=','),',1)'))
allcrops_summarisedcounts$rgba_svd<-gsub('rgba(211,211,211,1)','rgba(211,211,211,0.5)',allcrops_summarisedcounts$rgba_svd, fixed = TRUE)
plot_ly(
type = "sankey",
arrangement='snap',
#domain =list(
# x=c(0,1),
# y=c(0,1)
#),
orientation = "v",
# valueformat=".2f", #switch this on if doing per 6 trees otherwise it will rescale decimals
node = list(pad = 15,
thickness = 15,
line = list(color = "black", width = 0.5),
label = as.factor(node_col$name),
color=node_col$col_svd
),
link = list(source = allcrops_summarisedcounts$IDsource,
target = allcrops_summarisedcounts$IDtarget,
value = allcrops_summarisedcounts$average_count,
# value = test_merge_ave6trees$sum_6_tree),
color=allcrops_summarisedcounts$rgba_svd
)
) %>%
layout(font = list(size = 10),
xaxis = list(showgrid=F,zeroline=F),
yaxis=list(showgrid=F,zeroline=F))#intersect of both while ignoring two that didnt have good svd
top_10_taxa_svd<- top_10_taxa[top_10_taxa %in% svd_taxa]
node_col<-node_col %>% mutate(col_top10_svd=case_when(name %in% top_10_taxa_svd ~ color_hex, TRUE ~ '#D3D3D3'))
allcrops_summarisedcounts<-allcrops_summarisedcounts %>% mutate(col_top10_svd=case_when(Ordering %in% top_10_taxa_svd ~ color_hex, TRUE ~ '#D3D3D3'))
plot_ly(
type = "sankey",
orientation = "v",
# valueformat=".2f", #switch this on if doing per 6 trees otherwise it will rescale decimals
node = list(pad = 15,
thickness = 20,
line = list(color = "black", width = 0.5),
label = node_col$name,
color=node_col$col_top10_svd
),
link = list(source = allcrops_summarisedcounts$IDsource,
target = allcrops_summarisedcounts$IDtarget,
value = allcrops_summarisedcounts$average_count,
color=allcrops_summarisedcounts$col_top10_svd),
textfont = list(size = 10),
width = 700,
height = 500
) %>%
layout(font = list(size = 14),
margin = list(t = 40, l = 20, r = 10, b = 10))#to set grey opacity I need to switch to rgba format rather than hex
allcrops_summarisedcounts<-allcrops_summarisedcounts %>% rowwise() %>% mutate(rgba_top10_svd=paste0('rgba(',paste(col2rgb(col_top10_svd),collapse=','),',1)'))
allcrops_summarisedcounts$rgba_top10_svd<-gsub('rgba(211,211,211,1)','rgba(211,211,211,0.5)',allcrops_summarisedcounts$rgba_top10_svd, fixed = TRUE)
plot_ly(
type = "sankey",
arrangement='snap',
#domain =list(
# x=c(0,1),
# y=c(0,1)
#),
orientation = "v",
# valueformat=".2f", #switch this on if doing per 6 trees otherwise it will rescale decimals
node = list(pad = 15,
thickness = 15,
line = list(color = "black", width = 0.5),
label = as.factor(node_col$name),
color=node_col$col_top10_svd
),
link = list(source = allcrops_summarisedcounts$IDsource,
target = allcrops_summarisedcounts$IDtarget,
value = allcrops_summarisedcounts$average_count,
# value = test_merge_ave6trees$sum_6_tree),
color=allcrops_summarisedcounts$rgba_top10_svd
)
) %>%
layout(font = list(size = 10),
xaxis = list(showgrid=F,zeroline=F),
yaxis=list(showgrid=F,zeroline=F))#run without 24 as that one is a bit shit on SVD
top_10_taxa_svd_fil <- top_10_taxa_svd[!top_10_taxa_svd %in% c('24')]
node_col<-node_col %>% mutate(col_top10_svd_fil=case_when(name %in% top_10_taxa_svd_fil ~ color_hex, TRUE ~ '#D3D3D3'))
allcrops_summarisedcounts<-allcrops_summarisedcounts %>% mutate(col_top10_svd_fil=case_when(Ordering %in% top_10_taxa_svd_fil ~ color_hex, TRUE ~ '#D3D3D3'))
plot_ly(
type = "sankey",
orientation = "v",
# valueformat=".2f", #switch this on if doing per 6 trees otherwise it will rescale decimals
node = list(pad = 15,
thickness = 20,
line = list(color = "black", width = 0.5),
label = node_col$name,
color=node_col$col_top10_svd_fil
),
link = list(source = allcrops_summarisedcounts$IDsource,
target = allcrops_summarisedcounts$IDtarget,
value = allcrops_summarisedcounts$average_count,
color=allcrops_summarisedcounts$col_top10_svd_fil),
textfont = list(size = 10),
width = 700,
height = 500
) %>%
layout(font = list(size = 14),
margin = list(t = 40, l = 20, r = 10, b = 10))#to set grey opacity I need to switch to rgba format rather than hex
allcrops_summarisedcounts<-allcrops_summarisedcounts %>% rowwise() %>% mutate(rgba_top10_svd_fil=paste0('rgba(',paste(col2rgb(col_top10_svd_fil),collapse=','),',1)'))
allcrops_summarisedcounts$rgba_top10_svd_fil<-gsub('rgba(211,211,211,1)','rgba(211,211,211,0.5)',allcrops_summarisedcounts$rgba_top10_svd_fil, fixed = TRUE)
plot_ly(
type = "sankey",
arrangement='snap',
#domain =list(
# x=c(0,1),
# y=c(0,1)
#),
orientation = "v",
# valueformat=".2f", #switch this on if doing per 6 trees otherwise it will rescale decimals
node = list(pad = 15,
thickness = 15,
line = list(color = "black", width = 0.5),
label = as.factor(node_col$name),
color=node_col$col_top10_svd_fil
),
link = list(source = allcrops_summarisedcounts$IDsource,
target = allcrops_summarisedcounts$IDtarget,
value = allcrops_summarisedcounts$average_count,
# value = test_merge_ave6trees$sum_6_tree),
color=allcrops_summarisedcounts$rgba_top10_svd_fil
)
) %>%
layout(font = list(size = 10),
xaxis = list(showgrid=F,zeroline=F),
yaxis=list(showgrid=F,zeroline=F))Diversity statistics
Alpha diversity analyses on all data (regardless of replication number)
Species diversity
Eddy needs to double check that TukeyHSD is the most appropriate posthoc test on the anova analyses
#count species per property
sppr <- specnumber(counts_wide)
#head(sppr)
#sppr
#zeros are in there now, just because of the merge above they are out of order
####################################################
#impact of crop, region and flowering time
###################################################
#crop
sppr_aov <-aov(sppr ~ Crop, data = meta_data)
summary(sppr_aov)## Df Sum Sq Mean Sq F value Pr(>F)
## Crop 7 13718 1959.7 175.8 <2e-16 ***
## Residuals 420 4682 11.1
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(sppr_aov)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = sppr ~ Crop, data = meta_data)
##
## $Crop
## diff lwr upr p adj
## Avocado-Apple 4.8009185 3.03012342 6.571714 0.0000000
## Carrot-Apple 12.4536657 10.81629938 14.091032 0.0000000
## Kiwifruit-Apple 1.7479270 -0.03707788 3.532932 0.0598348
## Onion-Apple 14.8501493 12.94930758 16.750991 0.0000000
## Pak Choi-Apple 12.8481980 10.83142064 14.864975 0.0000000
## Pear-Apple 1.5451493 -0.48720733 3.577506 0.2872766
## Radish-Apple 6.2428765 2.93393591 9.551817 0.0000005
## Carrot-Avocado 7.6527473 6.00093630 9.304558 0.0000000
## Kiwifruit-Avocado -3.0529915 -4.85125544 -1.254727 0.0000099
## Onion-Avocado 10.0492308 8.13593260 11.962529 0.0000000
## Pak Choi-Avocado 8.0472795 6.01875746 10.075802 0.0000000
## Pear-Avocado -3.2557692 -5.29978100 -1.211757 0.0000469
## Radish-Avocado 1.4419580 -1.87415397 4.758070 0.8891829
## Kiwifruit-Carrot -10.7057387 -12.37277404 -9.038703 0.0000000
## Onion-Carrot 2.3964835 0.60596311 4.187004 0.0013998
## Pak Choi-Carrot 0.3945323 -1.51862094 2.307686 0.9984796
## Pear-Carrot -10.9085165 -12.83808575 -8.978947 0.0000000
## Radish-Carrot -6.2107892 -9.45761109 -2.963967 0.0000003
## Onion-Kiwifruit 13.1022222 11.17576504 15.028679 0.0000000
## Pak Choi-Kiwifruit 11.1002710 9.05933267 13.141209 0.0000000
## Pear-Kiwifruit -0.2027778 -2.25911226 1.853557 0.9999891
## Radish-Kiwifruit 4.4949495 1.17122775 7.818671 0.0011791
## Pak Choi-Onion -2.0019512 -4.14493647 0.141034 0.0867866
## Pear-Onion -13.3050000 -15.46265336 -11.147347 0.0000000
## Radish-Onion -8.6072727 -11.99461428 -5.219931 0.0000000
## Pear-Pak Choi -11.3030488 -13.56350449 -9.042593 0.0000000
## Radish-Pak Choi -6.6053215 -10.05905496 -3.151588 0.0000003
## Radish-Pear 4.6977273 1.23487339 8.160581 0.0011191#impact of region
sppr_aov <-aov(sppr ~ Region, data = meta_data)
summary(sppr_aov)## Df Sum Sq Mean Sq F value Pr(>F)
## Region 11 12574 1143 81.61 <2e-16 ***
## Residuals 416 5827 14
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(sppr_aov)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = sppr ~ Region, data = meta_data)
##
## $Region
## diff lwr upr p adj
## Bay of Plenty-Auckland -8.9932432 -15.3077536 -2.67873293 0.0002408
## Canterbury-Auckland -0.7282609 -6.9672304 5.51070866 0.9999998
## Hawkes Bay-Auckland -11.3768657 -17.7082694 -5.04546193 0.0000005
## Marlborough-Auckland 2.6309524 -4.0797595 9.34166425 0.9802373
## Northland-Auckland -8.3970588 -15.2329230 -1.56119469 0.0036773
## Otago-Auckland 4.3611111 -3.0308327 11.75305492 0.7338390
## Poverty Bay-Auckland -12.5000000 -20.0327554 -4.96724461 0.0000055
## Southland-Auckland 0.9166667 -6.4752771 8.30861048 0.9999997
## Tasman-Auckland -11.7954545 -18.1295600 -5.46134912 0.0000001
## Wairarapa-Auckland 0.4722222 -6.9197216 7.86416603 1.0000000
## Whangarei-Auckland -7.7500000 -15.6902214 0.19022136 0.0631329
## Canterbury-Bay of Plenty 8.2649824 6.4926272 10.03733755 0.0000000
## Hawkes Bay-Bay of Plenty -2.3836224 -4.4580321 -0.30921277 0.0098119
## Marlborough-Bay of Plenty 11.6241956 8.5827887 14.66560252 0.0000000
## Northland-Bay of Plenty 0.5961844 -2.7122201 3.90458895 0.9999854
## Otago-Bay of Plenty 13.3543544 9.0118510 17.69685771 0.0000000
## Poverty Bay-Bay of Plenty -3.5067568 -8.0848465 1.07133301 0.3318207
## Southland-Bay of Plenty 9.9099099 5.5674066 14.25241327 0.0000000
## Tasman-Bay of Plenty -2.8022113 -4.8848523 -0.71957027 0.0007566
## Wairarapa-Bay of Plenty 9.4654655 5.1229621 13.80796882 0.0000000
## Whangarei-Bay of Plenty 1.2432432 -3.9782142 6.46470065 0.9997633
## Hawkes Bay-Canterbury -10.6486048 -12.4802368 -8.81697282 0.0000000
## Marlborough-Canterbury 3.3592133 0.4779191 6.24050744 0.0080201
## Northland-Canterbury -7.6687980 -10.8306393 -4.50695659 0.0000000
## Otago-Canterbury 5.0893720 0.8574651 9.32127884 0.0050996
## Poverty Bay-Canterbury -11.7717391 -16.2450607 -7.29841753 0.0000000
## Southland-Canterbury 1.6449275 -2.5869793 5.87683439 0.9815174
## Tasman-Canterbury -11.0671937 -12.9081429 -9.22624448 0.0000000
## Wairarapa-Canterbury 1.2004831 -3.0314238 5.43238995 0.9987566
## Whangarei-Canterbury -7.0217391 -12.1515849 -1.89189335 0.0005428
## Marlborough-Hawkes Bay 14.0078181 10.9314909 17.08414520 0.0000000
## Northland-Hawkes Bay 2.9798068 -0.3607280 6.32034174 0.1336418
## Otago-Hawkes Bay 15.7379768 11.3709448 20.10500875 0.0000000
## Poverty Bay-Hawkes Bay -1.1231343 -5.7244970 3.47822837 0.9996990
## Southland-Hawkes Bay 12.2935323 7.9265004 16.66056431 0.0000000
## Tasman-Hawkes Bay -0.4185889 -2.5519024 1.71472463 0.9999653
## Wairarapa-Hawkes Bay 11.8490879 7.4820559 16.21611986 0.0000000
## Whangarei-Hawkes Bay 3.6268657 -1.6150090 8.86874036 0.4967873
## Northland-Marlborough -11.0280112 -15.0412585 -7.01476391 0.0000000
## Otago-Marlborough 1.7301587 -3.1706523 6.63096975 0.9914992
## Poverty Bay-Marlborough -15.1309524 -20.2416783 -10.02022648 0.0000000
## Southland-Marlborough -1.7142857 -6.6150967 3.18652531 0.9921280
## Tasman-Marlborough -14.4264069 -17.5082906 -11.34452325 0.0000000
## Wairarapa-Marlborough -2.1587302 -7.0595412 2.74208087 0.9531303
## Whangarei-Marlborough -10.3809524 -16.0751802 -4.68672454 0.0000003
## Otago-Northland 12.7581699 7.6873383 17.82900155 0.0000000
## Poverty Bay-Northland -4.1029412 -9.3769249 1.17104250 0.3079764
## Southland-Northland 9.3137255 4.2428939 14.38455711 0.0000002
## Tasman-Northland -3.3983957 -6.7440484 -0.05274308 0.0426937
## Wairarapa-Northland 8.8692810 3.7984494 13.94011266 0.0000011
## Whangarei-Northland 0.6470588 -5.1941410 6.48825864 0.9999999
## Poverty Bay-Otago -16.8611111 -22.8382926 -10.88392961 0.0000000
## Southland-Otago -3.4444444 -9.2431622 2.35427336 0.7252112
## Tasman-Otago -16.1565657 -20.5275137 -11.78561765 0.0000000
## Wairarapa-Otago -3.8888889 -9.6876067 1.90982892 0.5474530
## Whangarei-Otago -12.1111111 -18.5942747 -5.62794752 0.0000001
## Southland-Poverty Bay 13.4166667 7.4394852 19.39384817 0.0000000
## Tasman-Poverty Bay 0.7045455 -3.9005340 5.30962493 0.9999973
## Wairarapa-Poverty Bay 12.9722222 6.9950407 18.94940372 0.0000000
## Whangarei-Poverty Bay 4.7500000 -1.8932658 11.39326582 0.4427904
## Tasman-Southland -12.7121212 -17.0830692 -8.34117321 0.0000000
## Wairarapa-Southland -0.4444444 -6.2431622 5.35427336 1.0000000
## Whangarei-Southland -8.6666667 -15.1498303 -2.18350307 0.0008554
## Wairarapa-Tasman 12.2676768 7.8967288 16.63862477 0.0000000
## Whangarei-Tasman 4.0454545 -1.1996831 9.29059215 0.3211661
## Whangarei-Wairarapa -8.2222222 -14.7053858 -1.73905863 0.0021841#impact of crop & region
sppr_aov <-aov(sppr ~ Crop+Region, data = meta_data)
summary(sppr_aov)## Df Sum Sq Mean Sq F value Pr(>F)
## Crop 7 13718 1959.7 179.357 <2e-16 ***
## Region 11 213 19.4 1.776 0.056 .
## Residuals 409 4469 10.9
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(sppr_aov)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = sppr ~ Crop + Region, data = meta_data)
##
## $Crop
## diff lwr upr p adj
## Avocado-Apple 4.8009185 3.04761595 6.5542210 0.0000000
## Carrot-Apple 12.4536657 10.83247386 14.0748576 0.0000000
## Kiwifruit-Apple 1.7479270 -0.01944498 3.5152990 0.0550215
## Onion-Apple 14.8501493 12.96808476 16.7322137 0.0000000
## Pak Choi-Apple 12.8481980 10.85134307 14.8450530 0.0000000
## Pear-Apple 1.5451493 -0.46713100 3.5574295 0.2748478
## Radish-Apple 6.2428765 2.96662278 9.5191303 0.0000004
## Carrot-Avocado 7.6527473 6.01725347 9.2882410 0.0000000
## Kiwifruit-Avocado -3.0529915 -4.83349157 -1.2724913 0.0000077
## Onion-Avocado 10.0492308 8.15483283 11.9436287 0.0000000
## Pak Choi-Avocado 8.0472795 6.03879590 10.0557632 0.0000000
## Pear-Avocado -3.2557692 -5.27958954 -1.2319489 0.0000375
## Radish-Avocado 1.4419580 -1.84139626 4.7253123 0.8838097
## Kiwifruit-Carrot -10.7057387 -12.35630649 -9.0551709 0.0000000
## Onion-Carrot 2.3964835 0.62365050 4.1693165 0.0011906
## Pak Choi-Carrot 0.3945323 -1.49972215 2.2887867 0.9983774
## Pear-Carrot -10.9085165 -12.81902479 -8.9980082 0.0000000
## Radish-Carrot -6.2107892 -9.42553785 -2.9960406 0.0000002
## Onion-Kiwifruit 13.1022222 11.19479525 15.0096492 0.0000000
## Pak Choi-Kiwifruit 11.1002710 9.07949377 13.1210482 0.0000000
## Pear-Kiwifruit -0.2027778 -2.23879907 1.8332435 0.9999884
## Radish-Kiwifruit 4.4949495 1.20406063 7.7858384 0.0009996
## Pak Choi-Onion -2.0019512 -4.12376731 0.1198649 0.0805069
## Pear-Onion -13.3050000 -15.44133930 -11.1686607 0.0000000
## Radish-Onion -8.6072727 -11.96115293 -5.2533925 0.0000000
## Pear-Pak Choi -11.3030488 -13.54117492 -9.0649226 0.0000000
## Radish-Pak Choi -6.6053215 -10.02493778 -3.1857052 0.0000002
## Radish-Pear 4.6977273 1.26908067 8.1263739 0.0009478
##
## $Region
## diff lwr upr p adj
## Bay of Plenty-Auckland 0.37424883 -5.2034604 5.9519581 1.0000000
## Canterbury-Auckland -0.17062625 -5.6816091 5.3403566 1.0000000
## Hawkes Bay-Auckland 0.38450059 -5.2081309 5.9771321 1.0000000
## Marlborough-Auckland 0.62900116 -5.2986794 6.5566817 0.9999999
## Northland-Auckland -0.34977927 -6.3880088 5.6884503 1.0000000
## Otago-Auckland 3.42426368 -3.1051601 9.9536874 0.8563726
## Poverty Bay-Auckland -1.39972900 -8.0535339 5.2540759 0.9999300
## Southland-Auckland 0.91666667 -5.6127571 7.4460904 0.9999989
## Tasman-Auckland 0.02645322 -5.5685647 5.6214712 1.0000000
## Wairarapa-Auckland -1.52972900 -8.0591527 4.9996947 0.9997975
## Whangarei-Auckland 0.29727955 -6.7164466 7.3110057 1.0000000
## Canterbury-Bay of Plenty -0.54487507 -2.1104251 1.0206750 0.9924428
## Hawkes Bay-Bay of Plenty 0.01025177 -1.8221079 1.8426114 1.0000000
## Marlborough-Bay of Plenty 0.25475233 -2.4317717 2.9412763 1.0000000
## Northland-Bay of Plenty -0.72402810 -3.6463954 2.1983392 0.9996520
## Otago-Bay of Plenty 3.05001485 -0.7857887 6.8858184 0.2754018
## Poverty Bay-Bay of Plenty -1.77397782 -5.8178787 2.2699230 0.9544234
## Southland-Bay of Plenty 0.54241784 -3.2933857 4.3782214 0.9999988
## Tasman-Bay of Plenty -0.34779560 -2.1874262 1.4918350 0.9999762
## Wairarapa-Bay of Plenty -1.90397782 -5.7397814 1.9318258 0.8965318
## Whangarei-Bay of Plenty -0.07696928 -4.6891672 4.5352287 1.0000000
## Hawkes Bay-Canterbury 0.55512684 -1.0627834 2.1730370 0.9932973
## Marlborough-Canterbury 0.79962741 -1.7454664 3.3447212 0.9968698
## Northland-Canterbury -0.17915303 -2.9720587 2.6137527 1.0000000
## Otago-Canterbury 3.59488992 -0.1432220 7.3330018 0.0723046
## Poverty Bay-Canterbury -1.22910275 -5.1804602 2.7222547 0.9971349
## Southland-Canterbury 1.08729291 -2.6508190 4.8254048 0.9984336
## Tasman-Canterbury 0.19707947 -1.4290608 1.8232197 0.9999998
## Wairarapa-Canterbury -1.35910275 -5.0972146 2.3790091 0.9891512
## Whangarei-Canterbury 0.46790580 -4.0633701 4.9991817 1.0000000
## Marlborough-Hawkes Bay 0.24450057 -2.4728691 2.9618702 1.0000000
## Northland-Hawkes Bay -0.73427987 -3.6850285 2.2164687 0.9996370
## Otago-Hawkes Bay 3.03976308 -0.8177070 6.8972332 0.2886122
## Poverty Bay-Hawkes Bay -1.78422959 -5.8486878 2.2802286 0.9542000
## Southland-Hawkes Bay 0.53216607 -3.3253040 4.3896362 0.9999991
## Tasman-Hawkes Bay -0.35804737 -2.2424378 1.5263430 0.9999749
## Wairarapa-Hawkes Bay -1.91422959 -5.7716997 1.9432405 0.8966996
## Whangarei-Hawkes Bay -0.08722105 -4.7174539 4.5430118 1.0000000
## Northland-Marlborough -0.97878044 -4.5237468 2.5661859 0.9990304
## Otago-Marlborough 2.79526252 -1.5337033 7.1242283 0.6068617
## Poverty Bay-Marlborough -2.02873016 -6.5431172 2.4856568 0.9460531
## Southland-Marlborough 0.28766551 -4.0413003 4.6166313 1.0000000
## Tasman-Marlborough -0.60254794 -3.3248257 2.1197298 0.9998841
## Wairarapa-Marlborough -2.15873016 -6.4876959 2.1702356 0.8935406
## Whangarei-Marlborough -0.33172161 -5.3615253 4.6980821 1.0000000
## Otago-Northland 3.77404295 -0.7051048 8.2531907 0.1971117
## Poverty Bay-Northland -1.04994972 -5.7085450 3.6086455 0.9998614
## Southland-Northland 1.26644594 -3.2127018 5.7455937 0.9987919
## Tasman-Northland 0.37623250 -2.5790367 3.3315017 0.9999996
## Wairarapa-Northland -1.17994972 -5.6590975 3.2991980 0.9993756
## Whangarei-Northland 0.64705882 -4.5125676 5.8066853 0.9999997
## Poverty Bay-Otago -4.82399267 -10.1037339 0.4557486 0.1112829
## Southland-Otago -2.50759701 -7.6296984 2.6145044 0.9050631
## Tasman-Otago -3.39781045 -7.2587396 0.4631187 0.1473373
## Wairarapa-Otago -4.95399267 -10.0760941 0.1681087 0.0686706
## Whangarei-Otago -3.12698413 -8.8536676 2.5996993 0.8201319
## Southland-Poverty Bay 2.31639566 -2.9633456 7.5961369 0.9543845
## Tasman-Poverty Bay 1.42618222 -2.6415591 5.4939235 0.9919636
## Wairarapa-Poverty Bay -0.13000000 -5.4097413 5.1497413 1.0000000
## Whangarei-Poverty Bay 1.69700855 -4.1710958 7.5651129 0.9985140
## Tasman-Southland -0.89021344 -4.7511426 2.9707157 0.9998267
## Wairarapa-Southland -2.44639566 -7.5684971 2.6757057 0.9189074
## Whangarei-Southland -0.61938712 -6.3460706 5.1072963 0.9999999
## Wairarapa-Tasman -1.55618222 -5.4171114 2.3047470 0.9755242
## Whangarei-Tasman 0.27082633 -4.3622887 4.9039414 1.0000000
## Whangarei-Wairarapa 1.82700855 -3.8996749 7.5536920 0.9964181#region within a crop
#I did this for beta and then decided I prefer it because of the biased sampling across crops/regions
#radish has only one location
categories_crop <- meta_data %>% filter(Crop!='Radish') %>% select(Crop) %>% unique()
categories_crop$Crop <- gsub("Pak Choi", "Pakchoi", categories_crop$Crop)
for (item in categories_crop$Crop){
print(paste('Analysing',item))
#filtered_sppr <- sppr[grepl('^Apple', names(sppr))]
filtered_sppr <- sppr[grepl(paste0('^',item), names(sppr))]
filtered_meta_data<-meta_data %>% filter(sample_ID %in% names(filtered_sppr))
sppr_aov <-aov(filtered_sppr ~ Region, data = filtered_meta_data)
print(summary(sppr_aov))
print(TukeyHSD(sppr_aov))
}## [1] "Analysing Apple"
## Df Sum Sq Mean Sq F value Pr(>F)
## Region 1 0.19 0.1913 0.133 0.717
## Residuals 65 93.75 1.4423
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = filtered_sppr ~ Region, data = filtered_meta_data)
##
## $Region
## diff lwr upr p adj
## Tasman-Hawkes Bay -0.1114341 -0.7225623 0.4996941 0.7169195
##
## [1] "Analysing Pear"
## Df Sum Sq Mean Sq F value Pr(>F)
## Region 1 39.0 39.00 10.24 0.00278 **
## Residuals 38 144.8 3.81
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = filtered_sppr ~ Region, data = filtered_meta_data)
##
## $Region
## diff lwr upr p adj
## Tasman-Hawkes Bay -2.154762 -3.518116 -0.7914079 0.0027771
##
## [1] "Analysing Avocado"
## Df Sum Sq Mean Sq F value Pr(>F)
## Region 2 5.3 2.628 0.222 0.802
## Residuals 62 733.9 11.837
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = filtered_sppr ~ Region, data = filtered_meta_data)
##
## $Region
## diff lwr upr p adj
## Northland-Bay of Plenty -6.470588e-01 -3.021892 1.727774 0.7906585
## Whangarei-Bay of Plenty -8.881784e-16 -3.605591 3.605591 1.0000000
## Whangarei-Northland 6.470588e-01 -3.275956 4.570074 0.9172876
##
## [1] "Analysing Kiwifruit"
## Df Sum Sq Mean Sq F value Pr(>F)
## Region 3 50.81 16.937 5.257 0.00278 **
## Residuals 59 190.08 3.222
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = filtered_sppr ~ Region, data = filtered_meta_data)
##
## $Region
## diff lwr upr p adj
## Hawkes Bay-Bay of Plenty -1.6805556 -3.4710155 0.1099044306 0.0733401
## Poverty Bay-Bay of Plenty -1.8750000 -3.7507672 0.0007672466 0.0501313
## Tasman-Bay of Plenty 0.5892857 -0.9312928 2.1098642091 0.7357862
## Poverty Bay-Hawkes Bay -0.1944444 -2.5002737 2.1113848251 0.9960475
## Tasman-Hawkes Bay 2.2698413 0.2424033 4.2972792629 0.0223529
## Tasman-Poverty Bay 2.4642857 0.3611308 4.5674406341 0.0153827
##
## [1] "Analysing Pakchoi"
## Df Sum Sq Mean Sq F value Pr(>F)
## Region 3 45.1 15.03 0.907 0.447
## Residuals 37 613.3 16.58
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = filtered_sppr ~ Region, data = filtered_meta_data)
##
## $Region
## diff lwr upr p adj
## Canterbury-Auckland -0.5100000 -6.407272 5.387272 0.9954882
## Hawkes Bay-Auckland 3.2500000 -5.113934 11.613934 0.7242716
## Southland-Auckland 0.9166667 -5.664041 7.497375 0.9817868
## Hawkes Bay-Canterbury 3.7600000 -2.931147 10.451147 0.4410908
## Southland-Canterbury 1.4266667 -2.830301 5.683635 0.8041139
## Southland-Hawkes Bay -2.3333333 -9.633973 4.967307 0.8253941
##
## [1] "Analysing Onion"
## Df Sum Sq Mean Sq F value Pr(>F)
## Region 3 34.2 11.41 0.462 0.71
## Residuals 46 1137.0 24.72
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = filtered_sppr ~ Region, data = filtered_meta_data)
##
## $Region
## diff lwr upr p adj
## Marlborough-Canterbury 0.5142857 -3.965768 4.994340 0.9899256
## Otago-Canterbury 0.9333333 -5.910062 7.776729 0.9833602
## Wairarapa-Canterbury -1.6444444 -7.232053 3.943165 0.8611163
## Otago-Marlborough 0.4190476 -6.175413 7.013509 0.9982465
## Wairarapa-Marlborough -2.1587302 -7.438524 3.121064 0.6974441
## Wairarapa-Otago -2.5777778 -9.969490 4.813934 0.7891899
##
## [1] "Analysing Carrot"
## Df Sum Sq Mean Sq F value Pr(>F)
## Region 1 164.3 164.30 10.8 0.00146 **
## Residuals 89 1354.4 15.22
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = filtered_sppr ~ Region, data = filtered_meta_data)
##
## $Region
## diff lwr upr p adj
## Otago-Canterbury 6.554598 2.590838 10.51836 0.0014562#impact of flowering time
sppr_aov <-aov(sppr ~ flowertime, data = meta_data)
summary(sppr_aov)## Df Sum Sq Mean Sq F value Pr(>F)
## flowertime 1 12234 12234 845.3 <2e-16 ***
## Residuals 426 6166 14
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#TukeyHSD(sppr_aov)
#can plot that out
sppr_df <- sppr %>%
enframe() %>%
full_join(meta_data, by = c("name" = "sample_ID"))
#just picking a colour scheme for now will change depending on how we order the final plots
col_bound<-rcartocolor::carto_pal(n=9,name='Prism')
#col_bound<-c("#38381C", "#A04C00","#701C00","#285034","#E0C591","#E0DEDA","#CD951C","#7D99B5")
col_bound<-c("#6e7d3c", "#007b83","#b78080","#b5a53c","#E0C591","#516295","#CD951C","#6baed6")
#Trying to order by flowering time
#this is based of brads chart but note
#apple and pear flower at the same time so Radish, Carrot and Onion so they are ordered alphabetically
#so maybe its better to just say grouped by spring and summer flowering?
#sppr_df$Crop <- factor(sppr_df$Crop, levels = c("Apple","Pear","Avocado","Kiwifruit","Pak Choi","Carrot", "Onion","Radish"))
#reordering based on brads order
sppr_df$Crop <- factor(sppr_df$Crop, levels = c("Avocado","Apple","Pear","Kiwifruit","Pak Choi","Radish", "Carrot","Onion"))
plot_sppr <- ggplot(sppr_df, aes(x = Crop, y = value, fill = Crop)) +
geom_boxplot() +
scale_fill_manual(values=col_bound)+
theme_bw()+
labs(x = "Crop",
y = "Number of species per sample",
title = "Species richness")
plot_sppr
#other significant factor was region
plot_sppr <- ggplot(sppr_df, aes(x = Region, y = value, fill = Crop)) +
geom_boxplot() +
facet_wrap(~Crop, scales = "free_x")+
theme_bw()+
scale_fill_manual(values=col_bound)+
labs(x = "Region",
y = "Number of species per sample",
title = "Species richness")+
theme(axis.text.x = element_text(angle = 90,size=7,hjust=1))
plot_sppr
#other significant factor was flowertime
plot_sppr <- ggplot(sppr_df, aes(x = Crop, y = value, fill = Crop)) +
geom_boxplot() +
# facet_wrap(~flowertime, scales="free")+
facet_wrap(~flowertime, scales = "free_x")+
# facet_grid(.~flowertime,drop = TRUE) +
theme_bw()+
scale_fill_manual(values=col_bound)+
labs(x = "Region",
y = "Number of species per sample",
title = "Species richness")+
theme(axis.text.x = element_text(angle = 90,size=7,hjust=1))
plot_sppr
Simpson
#########
#simpson#
#########
#using simpson which is less sensitive to rare species which should be more robust to the sampling biases in the data
simpsondiv<-diversity(counts_wide,index='simpson')
#head(simpsondiv)
####################################################
#impact of crop, region and flowering time
###################################################
#crop
simpsondiv_aov <-aov(simpsondiv ~ Crop, data = meta_data)
summary(simpsondiv_aov)## Df Sum Sq Mean Sq F value Pr(>F)
## Crop 7 7.656 1.0937 15.66 <2e-16 ***
## Residuals 420 29.340 0.0699
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(simpsondiv_aov)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = simpsondiv ~ Crop, data = meta_data)
##
## $Crop
## diff lwr upr p adj
## Avocado-Apple -0.030217764 -0.170392206 0.109956677 0.9979801
## Carrot-Apple 0.236536499 0.106924146 0.366148851 0.0000013
## Kiwifruit-Apple -0.122952614 -0.264251894 0.018346665 0.1411042
## Onion-Apple 0.228728601 0.078259795 0.379197407 0.0001310
## Pak Choi-Apple 0.044205289 -0.115440878 0.203851456 0.9904551
## Pear-Apple -0.047350070 -0.208229471 0.113529330 0.9862868
## Radish-Apple -0.015809395 -0.277741965 0.246123176 0.9999996
## Carrot-Avocado 0.266754263 0.135998490 0.397510036 0.0000000
## Kiwifruit-Avocado -0.092734850 -0.235083705 0.049614005 0.4935943
## Onion-Avocado 0.258946365 0.107491514 0.410401216 0.0000082
## Pak Choi-Avocado 0.074423053 -0.086152813 0.234998919 0.8515717
## Pear-Avocado -0.017132306 -0.178934320 0.144669708 0.9999823
## Radish-Avocado 0.014408370 -0.248091881 0.276908621 0.9999998
## Kiwifruit-Carrot -0.359489113 -0.491450034 -0.227528192 0.0000000
## Onion-Carrot -0.007807898 -0.149543778 0.133927982 0.9999998
## Pak Choi-Carrot -0.192331210 -0.343774588 -0.040887832 0.0031638
## Pear-Carrot -0.283886569 -0.436629424 -0.131143714 0.0000008
## Radish-Carrot -0.252345893 -0.509361204 0.004669417 0.0585191
## Onion-Kiwifruit 0.351681215 0.199184709 0.504177721 0.0000000
## Pak Choi-Kiwifruit 0.167157903 0.005599179 0.328716627 0.0366488
## Pear-Kiwifruit 0.075602544 -0.087174924 0.238380012 0.8501762
## Radish-Kiwifruit 0.107143220 -0.155959411 0.370245850 0.9193118
## Pak Choi-Onion -0.184523312 -0.354159972 -0.014886652 0.0221842
## Pear-Onion -0.276078671 -0.446876444 -0.105280898 0.0000333
## Radish-Onion -0.244537995 -0.512676708 0.023600718 0.1033138
## Pear-Pak Choi -0.091555359 -0.270490868 0.087380150 0.7746063
## Radish-Pak Choi -0.060014683 -0.333408916 0.213379549 0.9977328
## Radish-Pear 0.031540676 -0.242575521 0.305656872 0.9999688#impact of region
simpsondiv_aov <-aov(simpsondiv ~ Region, data = meta_data)
summary(simpsondiv_aov)## Df Sum Sq Mean Sq F value Pr(>F)
## Region 11 6.694 0.6085 8.354 2.58e-13 ***
## Residuals 416 30.302 0.0728
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(simpsondiv_aov)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = simpsondiv ~ Region, data = meta_data)
##
## $Region
## diff lwr upr p adj
## Bay of Plenty-Auckland 0.078332291 -0.37704326 0.53370784 0.9999910
## Canterbury-Auckland 0.348921954 -0.10100591 0.79884982 0.3127820
## Hawkes Bay-Auckland 0.136202889 -0.32039094 0.59279672 0.9980333
## Marlborough-Auckland 0.365304571 -0.11864334 0.84925248 0.3548515
## Northland-Auckland 0.178888485 -0.31408488 0.67186185 0.9893351
## Otago-Auckland 0.436486777 -0.09658868 0.96956223 0.2342766
## Poverty Bay-Auckland -0.024253364 -0.56748355 0.51897682 1.0000000
## Southland-Auckland 0.139927882 -0.39314757 0.67300334 0.9993973
## Tasman-Auckland 0.128015112 -0.32877355 0.58480378 0.9988884
## Wairarapa-Auckland 0.363981971 -0.16909348 0.89705743 0.5184228
## Whangarei-Auckland 0.091202301 -0.48141259 0.66381720 0.9999959
## Canterbury-Bay of Plenty 0.270589664 0.14277497 0.39840436 0.0000000
## Hawkes Bay-Bay of Plenty 0.057870598 -0.09172698 0.20746817 0.9821965
## Marlborough-Bay of Plenty 0.286972280 0.06763899 0.50630557 0.0012694
## Northland-Bay of Plenty 0.100556194 -0.13803183 0.33914422 0.9658743
## Otago-Bay of Plenty 0.358154486 0.04499167 0.67131730 0.0104599
## Poverty Bay-Bay of Plenty -0.102585654 -0.43273796 0.22756665 0.9971664
## Southland-Bay of Plenty 0.061595591 -0.25156723 0.37475841 0.9999645
## Tasman-Bay of Plenty 0.049682822 -0.10050837 0.19987401 0.9951002
## Wairarapa-Bay of Plenty 0.285649681 -0.02751314 0.59881250 0.1127835
## Whangarei-Bay of Plenty 0.012870010 -0.36367922 0.38941925 1.0000000
## Hawkes Bay-Canterbury -0.212719065 -0.34480855 -0.08062958 0.0000126
## Marlborough-Canterbury 0.016382616 -0.19140403 0.22416926 1.0000000
## Northland-Canterbury -0.170033469 -0.39805198 0.05798504 0.3743062
## Otago-Canterbury 0.087564823 -0.21762225 0.39275189 0.9986196
## Poverty Bay-Canterbury -0.373175318 -0.69577219 -0.05057845 0.0089474
## Southland-Canterbury -0.208994073 -0.51418114 0.09619300 0.5136352
## Tasman-Canterbury -0.220906842 -0.35366825 -0.08814544 0.0000051
## Wairarapa-Canterbury 0.015060017 -0.29012705 0.32024709 1.0000000
## Whangarei-Canterbury -0.257719653 -0.62766225 0.11222294 0.4855772
## Marlborough-Hawkes Bay 0.229101682 0.00725009 0.45095327 0.0358971
## Northland-Hawkes Bay 0.042685596 -0.19821953 0.28359072 0.9999877
## Otago-Hawkes Bay 0.300283888 -0.01464783 0.61521561 0.0780311
## Poverty Bay-Hawkes Bay -0.160456253 -0.49228690 0.17137440 0.9122860
## Southland-Hawkes Bay 0.003724993 -0.31120672 0.31865671 1.0000000
## Tasman-Hawkes Bay -0.008187777 -0.16203325 0.14565769 1.0000000
## Wairarapa-Hawkes Bay 0.227779082 -0.08715263 0.54271080 0.4239433
## Whangarei-Hawkes Bay -0.045000588 -0.42302223 0.33302105 0.9999998
## Northland-Marlborough -0.186416086 -0.47583436 0.10300219 0.6108134
## Otago-Marlborough 0.071182206 -0.28224338 0.42460779 0.9999549
## Poverty Bay-Marlborough -0.389557934 -0.75812169 -0.02099418 0.0277943
## Southland-Marlborough -0.225376689 -0.57880227 0.12804890 0.6259751
## Tasman-Marlborough -0.237289458 -0.45954176 -0.01503715 0.0247692
## Wairarapa-Marlborough -0.001322599 -0.35474818 0.35210299 1.0000000
## Whangarei-Marlborough -0.274102270 -0.68474569 0.13654115 0.5549974
## Otago-Northland 0.257598292 -0.10808845 0.62328504 0.4673311
## Poverty Bay-Northland -0.203141849 -0.58347905 0.17719536 0.8408060
## Southland-Northland -0.038960603 -0.40464735 0.32672614 0.9999999
## Tasman-Northland -0.050873373 -0.29214757 0.19040082 0.9999285
## Wairarapa-Northland 0.185093486 -0.18059326 0.55078023 0.8835301
## Whangarei-Northland -0.087686184 -0.50892860 0.33355623 0.9999371
## Poverty Bay-Otago -0.460740141 -0.89178897 -0.02969131 0.0244425
## Southland-Otago -0.296558895 -0.71473769 0.12161990 0.4562860
## Tasman-Otago -0.308471665 -0.62368579 0.00674246 0.0615997
## Wairarapa-Otago -0.072504806 -0.49068360 0.34567399 0.9999902
## Whangarei-Otago -0.345284476 -0.81282258 0.12225363 0.3899233
## Southland-Poverty Bay 0.164181245 -0.26686759 0.59523008 0.9842455
## Tasman-Poverty Bay 0.152268476 -0.17983021 0.48436716 0.9381749
## Wairarapa-Poverty Bay 0.388235335 -0.04281350 0.81928417 0.1243299
## Whangarei-Poverty Bay 0.115455665 -0.36362833 0.59453966 0.9997340
## Tasman-Southland -0.011912769 -0.32712689 0.30330136 1.0000000
## Wairarapa-Southland 0.224054090 -0.19412470 0.64223288 0.8379997
## Whangarei-Southland -0.048725581 -0.51626368 0.41881252 1.0000000
## Wairarapa-Tasman 0.235966859 -0.07924727 0.55118098 0.3680999
## Whangarei-Tasman -0.036812811 -0.41506976 0.34144414 1.0000000
## Whangarei-Wairarapa -0.272779670 -0.74031777 0.19475843 0.7476537#impact of crop and region
simpsondiv_aov <-aov(simpsondiv ~ Crop+Region, data = meta_data)
summary(simpsondiv_aov)## Df Sum Sq Mean Sq F value Pr(>F)
## Crop 7 7.656 1.0937 15.49 <2e-16 ***
## Region 11 0.459 0.0417 0.59 0.837
## Residuals 409 28.882 0.0706
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(simpsondiv_aov)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = simpsondiv ~ Crop + Region, data = meta_data)
##
## $Crop
## diff lwr upr p adj
## Avocado-Apple -0.030217764 -0.171169414 0.110733885 0.9980476
## Carrot-Apple 0.236536499 0.106205500 0.366867497 0.0000016
## Kiwifruit-Apple -0.122952614 -0.265035339 0.019130110 0.1459932
## Onion-Apple 0.228728601 0.077425508 0.380031693 0.0001477
## Pak Choi-Apple 0.044205289 -0.116326049 0.204736626 0.9907559
## Pear-Apple -0.047350070 -0.209121479 0.114421339 0.9867105
## Radish-Apple -0.015809395 -0.279194271 0.247575482 0.9999996
## Carrot-Avocado 0.266754263 0.135273505 0.398235022 0.0000000
## Kiwifruit-Avocado -0.092734850 -0.235872970 0.050403270 0.5009102
## Onion-Avocado 0.258946365 0.106651761 0.411240969 0.0000096
## Pak Choi-Avocado 0.074423053 -0.087043139 0.235889245 0.8550822
## Pear-Avocado -0.017132306 -0.179831444 0.145566832 0.9999830
## Radish-Avocado 0.014408370 -0.249547334 0.278364074 0.9999998
## Kiwifruit-Carrot -0.359489113 -0.492181702 -0.226796525 0.0000000
## Onion-Carrot -0.007807898 -0.150329644 0.134713848 0.9999998
## Pak Choi-Carrot -0.192331210 -0.344614278 -0.040048142 0.0034355
## Pear-Carrot -0.283886569 -0.437476319 -0.130296819 0.0000009
## Radish-Carrot -0.252345893 -0.510786245 0.006094459 0.0612780
## Onion-Kiwifruit 0.351681215 0.198339180 0.505023250 0.0000000
## Pak Choi-Kiwifruit 0.167157903 0.004703404 0.329612402 0.0386116
## Pear-Kiwifruit 0.075602544 -0.088077457 0.239282545 0.8537127
## Radish-Kiwifruit 0.107143220 -0.157418204 0.371704644 0.9214315
## Pak Choi-Onion -0.184523312 -0.355100536 -0.013946088 0.0235222
## Pear-Onion -0.276078671 -0.447823446 -0.104333896 0.0000382
## Radish-Onion -0.244537995 -0.514163424 0.025087434 0.1073594
## Pear-Pak Choi -0.091555359 -0.271482990 0.088372272 0.7794090
## Radish-Pak Choi -0.060014683 -0.334924772 0.214895405 0.9978084
## Radish-Pear 0.031540676 -0.244095380 0.307176731 0.9999699
##
## $Region
## diff lwr upr p adj
## Bay of Plenty-Auckland 0.192856901 -0.2555468 0.6412606 0.9604167
## Canterbury-Auckland 0.212396567 -0.2306428 0.6554360 0.9169222
## Hawkes Bay-Auckland 0.203425469 -0.2461778 0.6530288 0.9434707
## Marlborough-Auckland 0.180781259 -0.2957573 0.6573199 0.9847207
## Northland-Auckland 0.253311538 -0.2321143 0.7387374 0.8604307
## Otago-Auckland 0.248493288 -0.2764207 0.7734073 0.9234532
## Poverty Bay-Auckland 0.142904540 -0.3920087 0.6778178 0.9992874
## Southland-Auckland 0.139927882 -0.3849861 0.6648419 0.9993018
## Tasman-Auckland 0.218389167 -0.2314060 0.6681843 0.9098978
## Wairarapa-Auckland 0.179458659 -0.3454553 0.7043727 0.9934981
## Whangarei-Auckland 0.165625354 -0.3982227 0.7294734 0.9982869
## Canterbury-Bay of Plenty 0.019539667 -0.1063182 0.1453975 0.9999969
## Hawkes Bay-Bay of Plenty 0.010568568 -0.1367386 0.1578758 1.0000000
## Marlborough-Bay of Plenty -0.012075642 -0.2280509 0.2038996 1.0000000
## Northland-Bay of Plenty 0.060454638 -0.1744806 0.2953898 0.9995000
## Otago-Bay of Plenty 0.055636388 -0.2527319 0.3640046 0.9999852
## Poverty Bay-Bay of Plenty -0.049952361 -0.3750500 0.2751453 0.9999972
## Southland-Bay of Plenty -0.052929019 -0.3612973 0.2554392 0.9999911
## Tasman-Bay of Plenty 0.025532267 -0.1223595 0.1734240 0.9999906
## Wairarapa-Bay of Plenty -0.013398241 -0.3217665 0.2949700 1.0000000
## Whangarei-Bay of Plenty -0.027231547 -0.3980158 0.3435527 1.0000000
## Hawkes Bay-Canterbury -0.008971098 -0.1390383 0.1210961 1.0000000
## Marlborough-Canterbury -0.031615308 -0.2362207 0.1729901 0.9999970
## Northland-Canterbury 0.040914971 -0.1836125 0.2654425 0.9999836
## Otago-Canterbury 0.036096721 -0.2644179 0.3366113 0.9999998
## Poverty Bay-Canterbury -0.069492028 -0.3871499 0.2481658 0.9998967
## Southland-Canterbury -0.072468686 -0.3729833 0.2280459 0.9997318
## Tasman-Canterbury 0.005992600 -0.1247362 0.1367214 1.0000000
## Wairarapa-Canterbury -0.032937908 -0.3334525 0.2675767 0.9999999
## Whangarei-Canterbury -0.046771213 -0.4110499 0.3175075 0.9999996
## Marlborough-Hawkes Bay -0.022644210 -0.2410992 0.1958108 1.0000000
## Northland-Hawkes Bay 0.049886069 -0.1873308 0.2871029 0.9999303
## Otago-Hawkes Bay 0.045067819 -0.2650423 0.3551779 0.9999984
## Poverty Bay-Hawkes Bay -0.060520929 -0.3872712 0.2662294 0.9999807
## Southland-Hawkes Bay -0.063497587 -0.3736077 0.2466125 0.9999466
## Tasman-Hawkes Bay 0.014963698 -0.1365264 0.1664538 1.0000000
## Wairarapa-Hawkes Bay -0.023966809 -0.3340769 0.2861433 1.0000000
## Whangarei-Hawkes Bay -0.037800115 -0.4100342 0.3344340 1.0000000
## Northland-Marlborough 0.072530279 -0.2124570 0.3575175 0.9995498
## Otago-Marlborough 0.067712029 -0.2803026 0.4157266 0.9999681
## Poverty Bay-Marlborough -0.037876719 -0.4007977 0.3250443 1.0000000
## Southland-Marlborough -0.040853377 -0.3888680 0.3071612 0.9999998
## Tasman-Marlborough 0.037607909 -0.1812417 0.2564575 0.9999910
## Wairarapa-Marlborough -0.001322599 -0.3493372 0.3466920 1.0000000
## Whangarei-Marlborough -0.015155905 -0.4195123 0.3892005 1.0000000
## Otago-Northland -0.004818250 -0.3649063 0.3552698 1.0000000
## Poverty Bay-Northland -0.110406999 -0.4849212 0.2641072 0.9982300
## Southland-Northland -0.113383657 -0.4734717 0.2467044 0.9968083
## Tasman-Northland -0.034922371 -0.2725026 0.2026579 0.9999982
## Wairarapa-Northland -0.073852879 -0.4339409 0.2862352 0.9999457
## Whangarei-Northland -0.087686184 -0.5024793 0.3271070 0.9999265
## Poverty Bay-Otago -0.105588749 -0.5300382 0.3188607 0.9996381
## Southland-Otago -0.108565407 -0.5203418 0.3032110 0.9993707
## Tasman-Otago -0.030104121 -0.3404923 0.2802840 1.0000000
## Wairarapa-Otago -0.069034629 -0.4808110 0.3427418 0.9999931
## Whangarei-Otago -0.082867934 -0.5432480 0.3775121 0.9999856
## Southland-Poverty Bay -0.002976658 -0.4274261 0.4214728 1.0000000
## Tasman-Poverty Bay 0.075484628 -0.2515296 0.4024988 0.9998248
## Wairarapa-Poverty Bay 0.036554120 -0.3878953 0.4610035 1.0000000
## Whangarei-Poverty Bay 0.022720815 -0.4490283 0.4944700 1.0000000
## Tasman-Southland 0.078461286 -0.2319269 0.3888494 0.9995780
## Wairarapa-Southland 0.039530778 -0.3722456 0.4513072 1.0000000
## Whangarei-Southland 0.025697472 -0.4346826 0.4860775 1.0000000
## Wairarapa-Tasman -0.038930508 -0.3493187 0.2714576 0.9999997
## Whangarei-Tasman -0.052763813 -0.4252296 0.3197020 0.9999988
## Whangarei-Wairarapa -0.013833305 -0.4742133 0.4465467 1.0000000#region within a crop
#I did this for beta and then decided I prefer it because of the biased sampling across crops/regions
#radish has only one location
categories_crop <- meta_data %>% filter(Crop!='Radish') %>% select(Crop) %>% unique()
categories_crop$Crop <- gsub("Pak Choi", "Pakchoi", categories_crop$Crop)
for (item in categories_crop$Crop){
print(paste('Analysing',item))
#filtered_sppr <- sppr[grepl('^Apple', names(sppr))]
filtered_simpsondiv <- simpsondiv[grepl(paste0('^',item), names(simpsondiv))]
filtered_meta_data<-meta_data %>% filter(sample_ID %in% names(filtered_simpsondiv))
simpsondiv_aov <-aov(filtered_simpsondiv ~ Region, data = filtered_meta_data)
print(summary(simpsondiv_aov))
print(TukeyHSD(simpsondiv_aov))
}## [1] "Analysing Apple"
## Df Sum Sq Mean Sq F value Pr(>F)
## Region 1 0.126 0.1259 0.673 0.415
## Residuals 65 12.154 0.1870
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = filtered_simpsondiv ~ Region, data = filtered_meta_data)
##
## $Region
## diff lwr upr p adj
## Tasman-Hawkes Bay -0.090412 -0.3104534 0.1296294 0.4148754
##
## [1] "Analysing Pear"
## Df Sum Sq Mean Sq F value Pr(>F)
## Region 1 0.232 0.2320 2.142 0.152
## Residuals 38 4.116 0.1083
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = filtered_simpsondiv ~ Region, data = filtered_meta_data)
##
## $Region
## diff lwr upr p adj
## Tasman-Hawkes Bay 0.1662014 -0.0636858 0.3960886 0.1515336
##
## [1] "Analysing Avocado"
## Df Sum Sq Mean Sq F value Pr(>F)
## Region 2 0.0605 0.03024 0.696 0.502
## Residuals 62 2.6924 0.04343
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = filtered_simpsondiv ~ Region, data = filtered_meta_data)
##
## $Region
## diff lwr upr p adj
## Northland-Bay of Plenty 0.06500406 -0.07883848 0.2088466 0.5267952
## Whangarei-Bay of Plenty -0.02268213 -0.24107114 0.1957069 0.9663120
## Whangarei-Northland -0.08768618 -0.32530142 0.1499290 0.6510261
##
## [1] "Analysing Kiwifruit"
## Df Sum Sq Mean Sq F value Pr(>F)
## Region 3 0.117 0.03883 0.365 0.778
## Residuals 59 6.275 0.10636
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = filtered_simpsondiv ~ Region, data = filtered_meta_data)
##
## $Region
## diff lwr upr p adj
## Hawkes Bay-Bay of Plenty -0.03729886 -0.3626249 0.2880272 0.9902293
## Poverty Bay-Bay of Plenty -0.05592347 -0.3967499 0.2849029 0.9724116
## Tasman-Bay of Plenty 0.07567469 -0.2006140 0.3519634 0.8870733
## Poverty Bay-Hawkes Bay -0.01862461 -0.4375931 0.4003439 0.9994128
## Tasman-Hawkes Bay 0.11297355 -0.2554114 0.4813585 0.8491254
## Tasman-Poverty Bay 0.13159816 -0.2505445 0.5137408 0.7993519
##
## [1] "Analysing Pakchoi"
## Df Sum Sq Mean Sq F value Pr(>F)
## Region 3 0.2918 0.09728 2.726 0.0579 .
## Residuals 37 1.3204 0.03569
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = filtered_simpsondiv ~ Region, data = filtered_meta_data)
##
## $Region
## diff lwr upr p adj
## Canterbury-Auckland 0.24280198 -0.03083279 0.51643674 0.0973993
## Hawkes Bay-Auckland 0.33413560 -0.05395281 0.72222400 0.1127644
## Southland-Auckland 0.13992788 -0.16541844 0.44527421 0.6105373
## Hawkes Bay-Canterbury 0.09133362 -0.21913710 0.40180435 0.8579440
## Southland-Canterbury -0.10287409 -0.30039837 0.09465018 0.5068087
## Southland-Hawkes Bay -0.19420772 -0.53295905 0.14454362 0.4235200
##
## [1] "Analysing Onion"
## Df Sum Sq Mean Sq F value Pr(>F)
## Region 3 0.0411 0.01370 0.847 0.475
## Residuals 46 0.7443 0.01618
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = filtered_simpsondiv ~ Region, data = filtered_meta_data)
##
## $Region
## diff lwr upr p adj
## Marlborough-Canterbury -0.050901985 -0.16552540 0.06372143 0.6400772
## Otago-Canterbury 0.023108232 -0.15198192 0.19819838 0.9848712
## Wairarapa-Canterbury -0.052224584 -0.19518509 0.09073593 0.7649682
## Otago-Marlborough 0.074010217 -0.09471087 0.24273131 0.6490195
## Wairarapa-Marlborough -0.001322599 -0.13640758 0.13376239 0.9999935
## Wairarapa-Otago -0.075332816 -0.26445179 0.11378616 0.7141963
##
## [1] "Analysing Carrot"
## Df Sum Sq Mean Sq F value Pr(>F)
## Region 1 0.0059 0.005854 0.668 0.416
## Residuals 89 0.7798 0.008762
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = filtered_simpsondiv ~ Region, data = filtered_meta_data)
##
## $Region
## diff lwr upr p adj
## Otago-Canterbury 0.03912555 -0.05598479 0.1342359 0.415892#impact of flowering time
simpsondiv_aov <-aov(simpsondiv ~ flowertime, data = meta_data)
summary(simpsondiv_aov)## Df Sum Sq Mean Sq F value Pr(>F)
## flowertime 1 5.54 5.540 75.03 <2e-16 ***
## Residuals 426 31.46 0.074
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#TukeyHSD(simpsondiv_aov)
#can plot that out
simpsondiv_df <- simpsondiv %>%
enframe() %>%
full_join(meta_data, by = c("name" = "sample_ID"))
#simpsondiv_df$Crop <- factor(simpsondiv_df$Crop, levels = c("Apple","Pear","Avocado","Kiwifruit","Pak Choi","Carrot", "Onion","Radish"))
#brads order
simpsondiv_df$Crop <- factor(simpsondiv_df$Crop, levels = c("Avocado","Apple","Pear","Kiwifruit","Pak Choi","Radish", "Carrot","Onion"))
#just crop
plot_sppr <- ggplot(simpsondiv_df, aes(x = Crop, y = value, fill = Crop)) +
geom_boxplot() +
scale_fill_manual(values=col_bound)+
theme_bw()+
labs(x = "Crop",
y = "Simpson",
title = "Species richness: simpson")
plot_sppr
#other significant factor was flowertime
plot_sppr <- ggplot(simpsondiv_df, aes(x = Crop, y = value, fill = Crop)) +
geom_boxplot() +
facet_wrap(~flowertime, scales = "free_x")+
theme_bw()+
scale_fill_manual(values=col_bound)+
labs(x = "Region",
y = "Simpson",
title = "Species richness: simpson")+
theme(axis.text.x = element_text(angle = 90,size=7,hjust=1))
plot_sppr
Shannon
I actually dont like Simpson, when say apple has a bunch of 1’s its getting a very high score as its perfectly even. So trying shannon as an alternative
#########
#shannon#
#########
#having a look at shannon
shannondiv<-diversity(counts_wide)
#head(shannondiv)
#crop
shannondiv_aov <-aov(shannondiv ~ Crop, data = meta_data)
summary(shannondiv_aov)## Df Sum Sq Mean Sq F value Pr(>F)
## Crop 7 193.02 27.57 131.3 <2e-16 ***
## Residuals 420 88.23 0.21
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1tukey_test<-TukeyHSD(shannondiv_aov)
tukey_test<-tukey_test$Crop
write.csv(tukey_test,'Alpha_shannon_diversity_allreplicates_countsvscrops.csv',quote = F,row.names = T)
#impact of region
shannondiv_aov <-aov(shannondiv ~ Region, data = meta_data)
summary(shannondiv_aov)## Df Sum Sq Mean Sq F value Pr(>F)
## Region 11 173.9 15.809 61.26 <2e-16 ***
## Residuals 416 107.4 0.258
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(shannondiv_aov)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = shannondiv ~ Region, data = meta_data)
##
## $Region
## diff lwr upr p adj
## Bay of Plenty-Auckland -0.020392176 -0.87752036 0.83673601 1.0000000
## Canterbury-Auckland 0.891560471 0.04468615 1.73843479 0.0290673
## Hawkes Bay-Auckland -0.547842393 -1.40726368 0.31157890 0.6265335
## Marlborough-Auckland 1.059372258 0.14846389 1.97028063 0.0083046
## Northland-Auckland 0.223678330 -0.70421814 1.15157480 0.9997332
## Otago-Auckland 1.303487394 0.30010897 2.30686582 0.0014415
## Poverty Bay-Auckland -0.734130209 -1.75662232 0.28836190 0.4359747
## Southland-Auckland 0.225384930 -0.77799349 1.22876335 0.9998662
## Tasman-Auckland -0.458749261 -1.31853728 0.40103875 0.8417196
## Wairarapa-Auckland 0.930325210 -0.07305321 1.93370363 0.0989330
## Whangarei-Auckland 0.075917532 -1.00188379 1.15371885 1.0000000
## Canterbury-Bay of Plenty 0.911952647 0.67137412 1.15253117 0.0000000
## Hawkes Bay-Bay of Plenty -0.527450217 -0.80902945 -0.24587098 0.0000001
## Marlborough-Bay of Plenty 1.079764434 0.66692552 1.49260335 0.0000000
## Northland-Bay of Plenty 0.244070506 -0.20501052 0.69315153 0.8247519
## Otago-Bay of Plenty 1.323879570 0.73443053 1.91332861 0.0000000
## Poverty Bay-Bay of Plenty -0.713738033 -1.33516544 -0.09231063 0.0098721
## Southland-Bay of Plenty 0.245777105 -0.34367193 0.83522614 0.9684699
## Tasman-Bay of Plenty -0.438357086 -0.72105364 -0.15566053 0.0000336
## Wairarapa-Bay of Plenty 0.950717385 0.36126835 1.54016642 0.0000121
## Whangarei-Bay of Plenty 0.096309708 -0.61244807 0.80506749 0.9999992
## Hawkes Bay-Canterbury -1.439402864 -1.68802759 -1.19077814 0.0000000
## Marlborough-Canterbury 0.167811787 -0.22329351 0.55891709 0.9611322
## Northland-Canterbury -0.667882141 -1.09706876 -0.23869552 0.0000308
## Otago-Canterbury 0.411926923 -0.16250981 0.98636365 0.4379991
## Poverty Bay-Canterbury -1.625690680 -2.23289691 -1.01848444 0.0000000
## Southland-Canterbury -0.666175541 -1.24061227 -0.09173881 0.0086395
## Tasman-Canterbury -1.350309732 -1.60019917 -1.10042029 0.0000000
## Wairarapa-Canterbury 0.038764739 -0.53567199 0.61320147 1.0000000
## Whangarei-Canterbury -0.815642939 -1.51196541 -0.11932047 0.0075035
## Marlborough-Hawkes Bay 1.607214651 1.18963568 2.02479362 0.0000000
## Northland-Hawkes Bay 0.771520723 0.31807834 1.22496311 0.0000026
## Otago-Hawkes Bay 1.851329787 1.25855125 2.44410832 0.0000000
## Poverty Bay-Hawkes Bay -0.186287816 -0.81087428 0.43829865 0.9980360
## Southland-Hawkes Bay 0.773227323 0.18044879 1.36600586 0.0013405
## Tasman-Hawkes Bay 0.089093132 -0.20048168 0.37866794 0.9974046
## Wairarapa-Hawkes Bay 1.478167603 0.88538907 2.07094614 0.0000000
## Whangarei-Hawkes Bay 0.623759925 -0.08776929 1.33528914 0.1514640
## Northland-Marlborough -0.835693928 -1.38044993 -0.29093793 0.0000439
## Otago-Marlborough 0.244115136 -0.42111828 0.90934855 0.9883161
## Poverty Bay-Marlborough -1.793502467 -2.48722961 -1.09977532 0.0000000
## Southland-Marlborough -0.833987328 -1.49922074 -0.16875391 0.0026477
## Tasman-Marlborough -1.518121519 -1.93645473 -1.09978831 0.0000000
## Wairarapa-Marlborough -0.129047048 -0.79428046 0.53618637 0.9999691
## Whangarei-Marlborough -0.983454726 -1.75638611 -0.21052334 0.0020673
## Otago-Northland 1.079809064 0.39149715 1.76812098 0.0000250
## Poverty Bay-Northland -0.957808539 -1.67369621 -0.24192087 0.0008419
## Southland-Northland 0.001706599 -0.68660532 0.69001852 1.0000000
## Tasman-Northland -0.682427592 -1.13656466 -0.22829053 0.0000723
## Wairarapa-Northland 0.706646880 0.01833496 1.39495880 0.0382084
## Whangarei-Northland -0.147760798 -0.94064208 0.64512048 0.9999795
## Poverty Bay-Otago -2.037617603 -2.84895696 -1.22627825 0.0000000
## Southland-Otago -1.078102464 -1.86521726 -0.29098767 0.0005359
## Tasman-Otago -1.762236655 -2.35554675 -1.16892656 0.0000000
## Wairarapa-Otago -0.373162184 -1.16027698 0.41395261 0.9227881
## Whangarei-Otago -1.227569862 -2.10759095 -0.34754877 0.0003714
## Southland-Poverty Bay 0.959515138 0.14817578 1.77085450 0.0065368
## Tasman-Poverty Bay 0.275380947 -0.34971003 0.90047192 0.9530844
## Wairarapa-Poverty Bay 1.664455418 0.85311606 2.47579478 0.0000000
## Whangarei-Poverty Bay 0.810047741 -0.09170554 1.71180102 0.1268297
## Tasman-Southland -0.684134191 -1.27744429 -0.09082409 0.0093530
## Wairarapa-Southland 0.704940280 -0.08217451 1.49205507 0.1297241
## Whangarei-Southland -0.149467397 -1.02948849 0.73055369 0.9999921
## Wairarapa-Tasman 1.389074471 0.79576437 1.98238457 0.0000000
## Whangarei-Tasman 0.534666794 -0.17730532 1.24663891 0.3630418
## Whangarei-Wairarapa -0.854407678 -1.73442877 0.02561341 0.0663055#impact of crop and region
shannondiv_aov <-aov(shannondiv ~ Crop+Region, data = meta_data)
summary(shannondiv_aov)## Df Sum Sq Mean Sq F value Pr(>F)
## Crop 7 193.02 27.575 135.843 <2e-16 ***
## Region 11 5.21 0.474 2.334 0.0086 **
## Residuals 409 83.02 0.203
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(shannondiv_aov)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = shannondiv ~ Crop + Region, data = meta_data)
##
## $Crop
## diff lwr upr p adj
## Avocado-Apple 0.89220666 0.65322760 1.1311857 0.0000000
## Carrot-Apple 1.78084911 1.55987703 2.0018212 0.0000000
## Kiwifruit-Apple 0.44980689 0.20891013 0.6907037 0.0000007
## Onion-Apple 1.83424985 1.57772025 2.0907795 0.0000000
## Pak Choi-Apple 1.22507545 0.95289965 1.4972512 0.0000000
## Pear-Apple 0.40659859 0.13232029 0.6808769 0.0002201
## Radish-Apple 0.94181391 0.49525320 1.3883746 0.0000000
## Carrot-Avocado 0.88864246 0.66572099 1.1115639 0.0000000
## Kiwifruit-Avocado -0.44239977 -0.68508591 -0.1997136 0.0000014
## Onion-Avocado 0.94204320 0.68383252 1.2002539 0.0000000
## Pak Choi-Avocado 0.33286879 0.05910797 0.6066296 0.0058453
## Pear-Avocado -0.48560806 -0.76145930 -0.2097568 0.0000038
## Radish-Avocado 0.04960726 -0.39792128 0.4971358 0.9999758
## Kiwifruit-Carrot -1.33104222 -1.55601830 -1.1060661 0.0000000
## Onion-Carrot 0.05340074 -0.18824037 0.2950418 0.9976312
## Pak Choi-Carrot -0.55577367 -0.81396479 -0.2975825 0.0000000
## Pear-Carrot -1.37425052 -1.63465708 -1.1138440 0.0000000
## Radish-Carrot -0.83903520 -1.27721263 -0.4008578 0.0000003
## Onion-Kiwifruit 1.38444296 1.12445640 1.6444295 0.0000000
## Pak Choi-Kiwifruit 0.77526855 0.49983210 1.0507050 0.0000000
## Pear-Kiwifruit -0.04320830 -0.32072256 0.2343060 0.9997592
## Radish-Kiwifruit 0.49200702 0.04345151 0.9405625 0.0202976
## Pak Choi-Onion -0.60917441 -0.89838269 -0.3199661 0.0000000
## Pear-Onion -1.42765126 -1.71883909 -1.1364634 0.0000000
## Radish-Onion -0.89243594 -1.34957731 -0.4352946 0.0000002
## Pear-Pak Choi -0.81847685 -1.12353845 -0.5134153 0.0000000
## Radish-Pak Choi -0.28326153 -0.74936288 0.1828398 0.5851131
## Radish-Pear 0.53521532 0.06788312 1.0025475 0.0124546
##
## $Region
## diff lwr upr p adj
## Bay of Plenty-Auckland 0.5037846203 -0.25646963 1.26403887 0.5664457
## Canterbury-Auckland 0.4975456275 -0.25361367 1.24870492 0.5671217
## Hawkes Bay-Auckland 0.4891334778 -0.27315471 1.25142167 0.6164521
## Marlborough-Auckland 0.4501978508 -0.35775827 1.25815397 0.7996824
## Northland-Auckland 0.5565471196 -0.26647708 1.37957132 0.5339770
## Otago-Auckland 0.7180466488 -0.17192842 1.60802171 0.2545594
## Poverty Bay-Auckland 0.0411383459 -0.86579015 0.94806684 1.0000000
## Southland-Auckland 0.2253849296 -0.66459014 1.11536000 0.9995706
## Tasman-Auckland 0.4984162280 -0.26419724 1.26102969 0.5881703
## Wairarapa-Auckland 0.3211508027 -0.56882426 1.21112587 0.9897996
## Whangarei-Auckland 0.4087863216 -0.54720025 1.36477289 0.9620413
## Canterbury-Bay of Plenty -0.0062389928 -0.21962697 0.20714898 1.0000000
## Hawkes Bay-Bay of Plenty -0.0146511425 -0.26440586 0.23510358 1.0000000
## Marlborough-Bay of Plenty -0.0535867695 -0.41976600 0.31259246 0.9999983
## Northland-Bay of Plenty 0.0527624993 -0.34556271 0.45108771 0.9999994
## Otago-Bay of Plenty 0.2142620285 -0.30856658 0.73709064 0.9722510
## Poverty Bay-Bay of Plenty -0.4626462744 -1.01383901 0.08854646 0.2018923
## Southland-Bay of Plenty -0.2783996907 -0.80122830 0.24442892 0.8433917
## Tasman-Bay of Plenty -0.0053683923 -0.25611416 0.24537737 1.0000000
## Wairarapa-Bay of Plenty -0.1826338176 -0.70546243 0.34019479 0.9922072
## Whangarei-Bay of Plenty -0.0949982987 -0.72365120 0.53365460 0.9999976
## Hawkes Bay-Canterbury -0.0084121497 -0.22893693 0.21211263 1.0000000
## Marlborough-Canterbury -0.0473477767 -0.39424976 0.29955421 0.9999992
## Northland-Canterbury 0.0590014921 -0.32167780 0.43968079 0.9999969
## Otago-Canterbury 0.2205010213 -0.28901199 0.73001404 0.9586172
## Poverty Bay-Canterbury -0.4564072816 -0.99498614 0.08217158 0.1900762
## Southland-Canterbury -0.2721606980 -0.78167371 0.23735232 0.8406182
## Tasman-Canterbury 0.0008706005 -0.22077595 0.22251716 1.0000000
## Wairarapa-Canterbury -0.1763948248 -0.68590784 0.33311819 0.9927713
## Whangarei-Canterbury -0.0887593059 -0.70638235 0.52886374 0.9999986
## Marlborough-Hawkes Bay -0.0389356270 -0.40931919 0.33144793 1.0000000
## Northland-Hawkes Bay 0.0674136418 -0.33477999 0.46960728 0.9999931
## Otago-Hawkes Bay 0.2289131710 -0.29686863 0.75469498 0.9568030
## Poverty Bay-Hawkes Bay -0.4479951319 -1.00198988 0.10599962 0.2513553
## Southland-Hawkes Bay -0.2637485483 -0.78953035 0.26203326 0.8896134
## Tasman-Hawkes Bay 0.0092827502 -0.24756388 0.26612938 1.0000000
## Wairarapa-Hawkes Bay -0.1679826751 -0.69376448 0.35779913 0.9963729
## Whangarei-Hawkes Bay -0.0803471562 -0.71145825 0.55076394 0.9999996
## Northland-Marlborough 0.1063492688 -0.37683758 0.58953612 0.9998903
## Otago-Marlborough 0.2678487980 -0.32219892 0.85789652 0.9421845
## Poverty Bay-Marlborough -0.4090595049 -1.02438055 0.20626154 0.5613299
## Southland-Marlborough -0.2248129212 -0.81486064 0.36523480 0.9841871
## Tasman-Marlborough 0.0482183772 -0.32283418 0.41927093 0.9999995
## Wairarapa-Marlborough -0.1290470481 -0.71909477 0.46100067 0.9998970
## Whangarei-Marlborough -0.0414115292 -0.72698503 0.64416197 1.0000000
## Otago-Northland 0.1614995292 -0.44901833 0.77201739 0.9993506
## Poverty Bay-Northland -0.5154087737 -1.15038573 0.11956818 0.2461470
## Southland-Northland -0.3311621901 -0.94168005 0.27935567 0.8264971
## Tasman-Northland -0.0581308916 -0.46094069 0.34467891 0.9999985
## Wairarapa-Northland -0.2353963170 -0.84591418 0.37512154 0.9826308
## Whangarei-Northland -0.1477607980 -0.85102943 0.55550783 0.9999309
## Poverty Bay-Otago -0.6769083029 -1.39654885 0.04273225 0.0877449
## Southland-Otago -0.4926617192 -1.19081560 0.20549216 0.4643080
## Tasman-Otago -0.2196304208 -0.74588371 0.30662287 0.9682190
## Wairarapa-Otago -0.3968958461 -1.09504973 0.30125803 0.7779120
## Whangarei-Otago -0.3092603272 -1.08982010 0.47129944 0.9785356
## Southland-Poverty Bay 0.1842465837 -0.53539396 0.90388713 0.9995235
## Tasman-Poverty Bay 0.4572778821 -0.09716436 1.01172013 0.2245281
## Wairarapa-Poverty Bay 0.2800124568 -0.43962809 0.99965301 0.9813450
## Whangarei-Poverty Bay 0.3676479757 -0.43218778 1.16748373 0.9370607
## Tasman-Southland 0.2730312985 -0.25322199 0.79928459 0.8650442
## Wairarapa-Southland 0.0957658731 -0.60238801 0.79391975 0.9999991
## Whangarei-Southland 0.1834013921 -0.59715838 0.96396116 0.9997917
## Wairarapa-Tasman -0.1772654254 -0.70351871 0.34898786 0.9942689
## Whangarei-Tasman -0.0896299064 -0.72113385 0.54187404 0.9999988
## Whangarei-Wairarapa 0.0876355189 -0.69292425 0.86819529 0.9999999#region within a crop
#I did this for beta and then decided I prefer it because of the biased sampling across crops/regions
#radish has only one location
categories_crop <- meta_data %>% filter(Crop!='Radish') %>% select(Crop) %>% unique()
categories_crop$Crop <- gsub("Pak Choi", "Pakchoi", categories_crop$Crop)
for (item in categories_crop$Crop){
print(paste('Analysing',item))
#filtered_sppr <- sppr[grepl('^Apple', names(sppr))]
filtered_shannondiv <- shannondiv[grepl(paste0('^',item), names(shannondiv))]
filtered_meta_data<-meta_data %>% filter(sample_ID %in% names(filtered_shannondiv))
shannondiv_aov <-aov(filtered_shannondiv ~ Region, data = filtered_meta_data)
print(summary(shannondiv_aov))
print(TukeyHSD(shannondiv_aov))
tukey_test<-TukeyHSD(shannondiv_aov)
tukey_test<-tukey_test$Region
write.csv(tukey_test,paste('Alpha_shannon_diversity_allreplicates_',item,'vsregion.csv'),quote = F,row.names = T)
}## [1] "Analysing Apple"
## Df Sum Sq Mean Sq F value Pr(>F)
## Region 1 0.007 0.00691 0.049 0.825
## Residuals 65 9.082 0.13973
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = filtered_shannondiv ~ Region, data = filtered_meta_data)
##
## $Region
## diff lwr upr p adj
## Tasman-Hawkes Bay -0.02117451 -0.211389 0.16904 0.8247621
##
## [1] "Analysing Pear"
## Df Sum Sq Mean Sq F value Pr(>F)
## Region 1 0.583 0.5828 1.91 0.175
## Residuals 38 11.594 0.3051
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = filtered_shannondiv ~ Region, data = filtered_meta_data)
##
## $Region
## diff lwr upr p adj
## Tasman-Hawkes Bay -0.263393 -0.6492039 0.1224178 0.175026
##
## [1] "Analysing Avocado"
## Df Sum Sq Mean Sq F value Pr(>F)
## Region 2 0.153 0.07655 0.306 0.738
## Residuals 62 15.527 0.25044
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = filtered_shannondiv ~ Region, data = filtered_meta_data)
##
## $Region
## diff lwr upr p adj
## Northland-Bay of Plenty 0.10007883 -0.2453594 0.4455170 0.7668904
## Whangarei-Bay of Plenty -0.04768197 -0.5721437 0.4767798 0.9740791
## Whangarei-Northland -0.14776080 -0.7183943 0.4228727 0.8087646
##
## [1] "Analysing Kiwifruit"
## Df Sum Sq Mean Sq F value Pr(>F)
## Region 3 3.966 1.3221 4.926 0.00404 **
## Residuals 59 15.836 0.2684
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = filtered_shannondiv ~ Region, data = filtered_meta_data)
##
## $Region
## diff lwr upr p adj
## Hawkes Bay-Bay of Plenty -0.44076165 -0.95755841 0.07603511 0.1206497
## Poverty Bay-Bay of Plenty -0.52474895 -1.06616873 0.01667082 0.0607565
## Tasman-Bay of Plenty 0.18879651 -0.25010190 0.62769492 0.6681835
## Poverty Bay-Hawkes Bay -0.08398731 -0.74953979 0.58156518 0.9870700
## Tasman-Hawkes Bay 0.62955816 0.04436028 1.21475604 0.0302723
## Tasman-Poverty Bay 0.71354546 0.10649272 1.32059821 0.0149710
##
## [1] "Analysing Pakchoi"
## Df Sum Sq Mean Sq F value Pr(>F)
## Region 3 2.433 0.8110 3.332 0.0298 *
## Residuals 37 9.007 0.2434
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = filtered_shannondiv ~ Region, data = filtered_meta_data)
##
## $Region
## diff lwr upr p adj
## Canterbury-Auckland 0.5910218 -0.12363480 1.3056784 0.1354441
## Hawkes Bay-Auckland 0.9346287 -0.07894837 1.9482058 0.0798167
## Southland-Auckland 0.2253849 -0.57209327 1.0228631 0.8717124
## Hawkes Bay-Canterbury 0.3436070 -0.46725473 1.1544686 0.6676674
## Southland-Canterbury -0.3656368 -0.88151436 0.1502407 0.2429283
## Southland-Hawkes Bay -0.7092438 -1.59396644 0.1754788 0.1546168
##
## [1] "Analysing Onion"
## Df Sum Sq Mean Sq F value Pr(>F)
## Region 3 0.495 0.1652 0.909 0.444
## Residuals 46 8.359 0.1817
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = filtered_shannondiv ~ Region, data = filtered_meta_data)
##
## $Region
## diff lwr upr p adj
## Marlborough-Canterbury -0.09754088 -0.4816738 0.2865921 0.9053416
## Otago-Canterbury 0.12255610 -0.4642167 0.7093289 0.9442408
## Wairarapa-Canterbury -0.22658792 -0.7056859 0.2525100 0.5921865
## Otago-Marlborough 0.22009698 -0.3453314 0.7855254 0.7284405
## Wairarapa-Marlborough -0.12904705 -0.5817521 0.3236580 0.8719835
## Wairarapa-Otago -0.34914403 -0.9829310 0.2846430 0.4645013
##
## [1] "Analysing Carrot"
## Df Sum Sq Mean Sq F value Pr(>F)
## Region 1 0.375 0.3753 3.605 0.0608 .
## Residuals 89 9.265 0.1041
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = filtered_shannondiv ~ Region, data = filtered_meta_data)
##
## $Region
## diff lwr upr p adj
## Otago-Canterbury 0.313265 -0.01456642 0.6410964 0.060845#impact of flowering time
shannondiv_aov <-aov(shannondiv ~ flowertime, data = meta_data)
summary(shannondiv_aov)## Df Sum Sq Mean Sq F value Pr(>F)
## flowertime 1 150.6 150.62 491.2 <2e-16 ***
## Residuals 426 130.6 0.31
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#TukeyHSD(shannondiv_aov)
#can plot that out
shannondiv_df <- shannondiv %>%
enframe() %>%
full_join(meta_data, by = c("name" = "sample_ID"))
#shannondiv_df$Crop <- factor(shannondiv_df$Crop, levels = c("Apple","Pear","Avocado","Kiwifruit","Pak Choi","Carrot", "Onion","Radish"))
#brads order
shannondiv_df$Crop <- factor(shannondiv_df$Crop, levels = c("Avocado","Apple","Pear","Kiwifruit","Pak Choi","Radish", "Carrot","Onion"))
#just crop
plot_sppr <- ggplot(shannondiv_df, aes(x = Crop, y = value, fill = Crop)) +
geom_boxplot() +
scale_fill_manual(values=col_bound)+
theme_bw()+
labs(x = "Crop",
y = "Shannon",
title = "Species richness: shannon")
plot_sppr
#other significant factor was region
plot_sppr <- ggplot(shannondiv_df, aes(x = Region, y = value, fill = Crop)) +
geom_boxplot() +
facet_wrap(~Crop, scales = "free_x")+
theme_bw()+
scale_fill_manual(values=col_bound)+
labs(x = "Region",
y = "Shannon",
title = "Species richness: shannon")+
theme(axis.text.x = element_text(angle = 90,size=7,hjust=1))
plot_sppr
#other significant factor was flowertime
plot_sppr <- ggplot(shannondiv_df, aes(x = Crop, y = value, fill = Crop)) +
geom_boxplot() +
facet_wrap(~flowertime, scales = "free_x")+
theme_bw()+
scale_fill_manual(values=col_bound)+
labs(x = "Region",
y = "Shannon",
title = "Species richness: shannon")+
theme(axis.text.x = element_text(angle = 90,size=7,hjust=1))
plot_sppr
Beta diversity and MDS plots
I have to remove the 0’s (e.g. samples where there was no non-bee insects observed ~mostly apple/pear) but here it is: After reading this https://stats.stackexchange.com/questions/639594/permanova-with-vegan-adonis-for-three-factors-and-repeated-measures im trying to avoid us having to look at multi factors for beta diversity but we can chat about that if needed. For now I’ve removed them.
################
#beta diversity#
################
#remove zero values:
counts_wide_no0 <- counts_wide[rowSums(counts_wide == 0) != ncol(counts_wide), ]
meta_data_no0<-meta_data %>% filter(sample_ID %in% row.names(counts_wide_no0))
#crop
perm<-adonis2(counts_wide_no0 ~ Crop,data=meta_data_no0)
perm## Permutation test for adonis under reduced model
## Permutation: free
## Number of permutations: 999
##
## adonis2(formula = counts_wide_no0 ~ Crop, data = meta_data_no0)
## Df SumOfSqs R2 F Pr(>F)
## Model 7 38.567 0.24426 17.5 0.001 ***
## Residual 379 119.325 0.75574
## Total 386 157.892 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1pairwise_adonis<-pairwise.adonis2(counts_wide_no0 ~ Crop, data = meta_data_no0)
tests<-names(pairwise_adonis)
tests<-tests[-1]
columns= c("Test","pvalue")
myData = data.frame(matrix(nrow = 0, ncol = length(columns)))
for (item in tests){
# print(item)
pvalue<-eval(parse(text =paste0('pairwise_adonis$','`',item,'`' ,'$`Pr(>F)`[1]')))
# print(pvalue)
out<-data.frame(Test = item, pvalue = pvalue)
#print(out)
myData<-rbind(myData,out)
}
write.csv(myData,'Pairwise_adonis_beta_diversity_allreplicates_countsvscrops.csv',quote = F,row.names = F)
#flowertime
perm<-adonis2(counts_wide_no0 ~ flowertime,data=meta_data_no0)
perm## Permutation test for adonis under reduced model
## Permutation: free
## Number of permutations: 999
##
## adonis2(formula = counts_wide_no0 ~ flowertime, data = meta_data_no0)
## Df SumOfSqs R2 F Pr(>F)
## Model 1 17.958 0.11374 49.408 0.001 ***
## Residual 385 139.934 0.88626
## Total 386 157.892 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#pairwise_adonis<-pairwise.adonis2(counts_wide_no0 ~ flowertime, data = meta_data_no0)
#pairwise_adonis
#I think to look at the impact of region within a crop we need to run this within a crop. This is because there is little overlap in regions between crops and the 0,0 situation makes it too confusing (at least for me)
#so just testing does community composition vary between regions within a crop.
categories_crop <- meta_data %>% filter(Crop!='Radish') %>% select(Crop) %>% unique()
categories_crop$Crop <- gsub("Pak Choi", "Pakchoi", categories_crop$Crop)
for (item in categories_crop$Crop){
print(paste('Analysing',item))
filtered_counts_wide_no0 <- counts_wide_no0[grepl(paste0('^',item), rownames(counts_wide_no0)), ] %>% select(where(~ sum(.) != 0))
filtered_meta_data_no0<-meta_data %>% filter(sample_ID %in% row.names(filtered_counts_wide_no0))
perm<-adonis2(filtered_counts_wide_no0 ~ Region,data=filtered_meta_data_no0)
print(perm)
pairwise_adonis<-pairwise.adonis2(filtered_counts_wide_no0 ~ Region, data = filtered_meta_data_no0)
print(pairwise_adonis)
tests<-names(pairwise_adonis)
tests<-tests[-1]
columns= c("Test","pvalue")
myData = data.frame(matrix(nrow = 0, ncol = length(columns)))
for (item1 in tests){
# print(item)
pvalue<-eval(parse(text =paste0('pairwise_adonis$','`',item1,'`' ,'$`Pr(>F)`[1]')))
# print(pvalue)
out<-data.frame(Test = item1, pvalue = pvalue)
#print(out)
myData<-rbind(myData,out)
}
write.csv(myData,paste0('Pairwise_adonis_beta_diversity_allreplicates_regionvs',item,'.csv'),quote = F,row.names = F)
}## [1] "Analysing Apple"
## Permutation test for adonis under reduced model
## Permutation: free
## Number of permutations: 999
##
## adonis2(formula = filtered_counts_wide_no0 ~ Region, data = filtered_meta_data_no0)
## Df SumOfSqs R2 F Pr(>F)
## Model 1 2.1146 0.13658 5.6945 0.001 ***
## Residual 36 13.3686 0.86342
## Total 37 15.4832 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## $parent_call
## [1] "filtered_counts_wide_no0 ~ Region , strata = Null , permutations 999"
##
## $`Hawkes Bay_vs_Tasman`
## Df SumOfSqs R2 F Pr(>F)
## Model 1 2.1146 0.13658 5.6945 0.001 ***
## Residual 36 13.3686 0.86342
## Total 37 15.4832 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## attr(,"class")
## [1] "pwadstrata" "list"
## [1] "Analysing Pear"
## Permutation test for adonis under reduced model
## Permutation: free
## Number of permutations: 999
##
## adonis2(formula = filtered_counts_wide_no0 ~ Region, data = filtered_meta_data_no0)
## Df SumOfSqs R2 F Pr(>F)
## Model 1 2.3614 0.19972 7.7365 0.001 ***
## Residual 31 9.4620 0.80028
## Total 32 11.8234 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## $parent_call
## [1] "filtered_counts_wide_no0 ~ Region , strata = Null , permutations 999"
##
## $`Hawkes Bay_vs_Tasman`
## Df SumOfSqs R2 F Pr(>F)
## Model 1 2.3614 0.19972 7.7365 0.001 ***
## Residual 31 9.4620 0.80028
## Total 32 11.8234 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## attr(,"class")
## [1] "pwadstrata" "list"
## [1] "Analysing Avocado"
## Permutation test for adonis under reduced model
## Permutation: free
## Number of permutations: 999
##
## adonis2(formula = filtered_counts_wide_no0 ~ Region, data = filtered_meta_data_no0)
## Df SumOfSqs R2 F Pr(>F)
## Model 2 1.984 0.09929 3.4172 0.001 ***
## Residual 62 17.999 0.90071
## Total 64 19.983 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## $parent_call
## [1] "filtered_counts_wide_no0 ~ Region , strata = Null , permutations 999"
##
## $`Bay of Plenty_vs_Northland`
## Df SumOfSqs R2 F Pr(>F)
## Model 1 1.6091 0.08728 5.4506 0.001 ***
## Residual 57 16.8276 0.91272
## Total 58 18.4368 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $`Bay of Plenty_vs_Whangarei`
## Df SumOfSqs R2 F Pr(>F)
## Model 1 0.3286 0.023 1.083 0.357
## Residual 46 13.9555 0.977
## Total 47 14.2841 1.000
##
## $Northland_vs_Whangarei
## Df SumOfSqs R2 F Pr(>F)
## Model 1 0.7935 0.13209 3.196 0.002 **
## Residual 21 5.2140 0.86791
## Total 22 6.0075 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## attr(,"class")
## [1] "pwadstrata" "list"
## [1] "Analysing Kiwifruit"
## Permutation test for adonis under reduced model
## Permutation: free
## Number of permutations: 999
##
## adonis2(formula = filtered_counts_wide_no0 ~ Region, data = filtered_meta_data_no0)
## Df SumOfSqs R2 F Pr(>F)
## Model 3 3.0124 0.14317 3.0077 0.001 ***
## Residual 54 18.0280 0.85683
## Total 57 21.0404 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## $parent_call
## [1] "filtered_counts_wide_no0 ~ Region , strata = Null , permutations 999"
##
## $`Bay of Plenty_vs_Poverty Bay`
## Df SumOfSqs R2 F Pr(>F)
## Model 1 0.5005 0.03724 1.354 0.201
## Residual 35 12.9385 0.96276
## Total 36 13.4390 1.00000
##
## $`Bay of Plenty_vs_Hawkes Bay`
## Df SumOfSqs R2 F Pr(>F)
## Model 1 1.2985 0.09109 3.6079 0.002 **
## Residual 36 12.9567 0.90891
## Total 37 14.2552 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $`Bay of Plenty_vs_Tasman`
## Df SumOfSqs R2 F Pr(>F)
## Model 1 1.3003 0.08221 3.8516 0.002 **
## Residual 43 14.5169 0.91779
## Total 44 15.8172 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $`Poverty Bay_vs_Hawkes Bay`
## Df SumOfSqs R2 F Pr(>F)
## Model 1 0.9114 0.20608 2.8553 0.026 *
## Residual 11 3.5112 0.79392
## Total 12 4.4226 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $`Poverty Bay_vs_Tasman`
## Df SumOfSqs R2 F Pr(>F)
## Model 1 0.6616 0.1154 2.3482 0.026 *
## Residual 18 5.0713 0.8846
## Total 19 5.7329 1.0000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $`Hawkes Bay_vs_Tasman`
## Df SumOfSqs R2 F Pr(>F)
## Model 1 1.1077 0.17874 4.1351 0.004 **
## Residual 19 5.0895 0.82126
## Total 20 6.1972 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## attr(,"class")
## [1] "pwadstrata" "list"
## [1] "Analysing Pakchoi"
## Permutation test for adonis under reduced model
## Permutation: free
## Number of permutations: 999
##
## adonis2(formula = filtered_counts_wide_no0 ~ Region, data = filtered_meta_data_no0)
## Df SumOfSqs R2 F Pr(>F)
## Model 3 2.5275 0.19973 3.0781 0.001 ***
## Residual 37 10.1273 0.80027
## Total 40 12.6548 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## Set of permutations < 'minperm'. Generating entire set.## $parent_call
## [1] "filtered_counts_wide_no0 ~ Region , strata = Null , permutations 999"
##
## $Auckland_vs_Canterbury
## Df SumOfSqs R2 F Pr(>F)
## Model 1 1.1250 0.13494 4.2115 0.001 ***
## Residual 27 7.2122 0.86506
## Total 28 8.3372 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $`Auckland_vs_Hawkes Bay`
## Df SumOfSqs R2 F Pr(>F)
## Model 1 0.3954 0.22901 1.4852 0.219
## Residual 5 1.3312 0.77099
## Total 6 1.7265 1.00000
##
## $Auckland_vs_Southland
## Df SumOfSqs R2 F Pr(>F)
## Model 1 0.9536 0.22868 3.2612 0.002 **
## Residual 11 3.2165 0.77132
## Total 12 4.1702 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $`Canterbury_vs_Hawkes Bay`
## Df SumOfSqs R2 F Pr(>F)
## Model 1 0.5671 0.07584 2.1335 0.035 *
## Residual 26 6.9107 0.92416
## Total 27 7.4778 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $Canterbury_vs_Southland
## Df SumOfSqs R2 F Pr(>F)
## Model 1 0.9286 0.09549 3.3781 0.001 ***
## Residual 32 8.7961 0.90451
## Total 33 9.7247 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $`Hawkes Bay_vs_Southland`
## Df SumOfSqs R2 F Pr(>F)
## Model 1 0.6992 0.19346 2.3987 0.024 *
## Residual 10 2.9150 0.80654
## Total 11 3.6142 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## attr(,"class")
## [1] "pwadstrata" "list"
## [1] "Analysing Onion"
## Permutation test for adonis under reduced model
## Permutation: free
## Number of permutations: 999
##
## adonis2(formula = filtered_counts_wide_no0 ~ Region, data = filtered_meta_data_no0)
## Df SumOfSqs R2 F Pr(>F)
## Model 3 1.6693 0.12486 2.1877 0.003 **
## Residual 46 11.6996 0.87514
## Total 49 13.3689 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## $parent_call
## [1] "filtered_counts_wide_no0 ~ Region , strata = Null , permutations 999"
##
## $Canterbury_vs_Otago
## Df SumOfSqs R2 F Pr(>F)
## Model 1 0.4072 0.09027 1.786 0.065 .
## Residual 18 4.1038 0.90973
## Total 19 4.5110 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $Canterbury_vs_Marlborough
## Df SumOfSqs R2 F Pr(>F)
## Model 1 0.6737 0.07373 2.7063 0.005 **
## Residual 34 8.4640 0.92627
## Total 35 9.1378 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $Canterbury_vs_Wairarapa
## Df SumOfSqs R2 F Pr(>F)
## Model 1 0.6049 0.09762 2.3801 0.006 **
## Residual 22 5.5909 0.90238
## Total 23 6.1958 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $Otago_vs_Marlborough
## Df SumOfSqs R2 F Pr(>F)
## Model 1 0.3443 0.05336 1.3528 0.173
## Residual 24 6.1087 0.94664
## Total 25 6.4530 1.00000
##
## $Otago_vs_Wairarapa
## Df SumOfSqs R2 F Pr(>F)
## Model 1 0.4502 0.12215 1.6698 0.065 .
## Residual 12 3.2355 0.87785
## Total 13 3.6858 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $Marlborough_vs_Wairarapa
## Df SumOfSqs R2 F Pr(>F)
## Model 1 0.7098 0.08546 2.6166 0.007 **
## Residual 28 7.5958 0.91454
## Total 29 8.3056 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## attr(,"class")
## [1] "pwadstrata" "list"
## [1] "Analysing Carrot"
## Permutation test for adonis under reduced model
## Permutation: free
## Number of permutations: 999
##
## adonis2(formula = filtered_counts_wide_no0 ~ Region, data = filtered_meta_data_no0)
## Df SumOfSqs R2 F Pr(>F)
## Model 1 0.4846 0.02243 2.0424 0.029 *
## Residual 89 21.1162 0.97757
## Total 90 21.6008 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## $parent_call
## [1] "filtered_counts_wide_no0 ~ Region , strata = Null , permutations 999"
##
## $Canterbury_vs_Otago
## Df SumOfSqs R2 F Pr(>F)
## Model 1 0.4846 0.02243 2.0424 0.025 *
## Residual 89 21.1162 0.97757
## Total 90 21.6008 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## attr(,"class")
## [1] "pwadstrata" "list"#MDS
pk_NMDS <- metaMDS(counts_wide_no0,trymax=200)## Square root transformation
## Wisconsin double standardization
## Run 0 stress 0.1808728
## Run 1 stress 0.1811676
## ... Procrustes: rmse 0.02621764 max resid 0.3884263
## Run 2 stress 0.1811394
## ... Procrustes: rmse 0.03229755 max resid 0.3547199
## Run 3 stress 0.1824531
## Run 4 stress 0.181229
## ... Procrustes: rmse 0.01533358 max resid 0.1312302
## Run 5 stress 0.1810326
## ... Procrustes: rmse 0.02088862 max resid 0.255728
## Run 6 stress 0.1803824
## ... New best solution
## ... Procrustes: rmse 0.01288165 max resid 0.1688527
## Run 7 stress 0.1813274
## Run 8 stress 0.1800154
## ... New best solution
## ... Procrustes: rmse 0.01365318 max resid 0.2111474
## Run 9 stress 0.1840188
## Run 10 stress 0.1829942
## Run 11 stress 0.1826043
## Run 12 stress 0.1824726
## Run 13 stress 0.1831849
## Run 14 stress 0.1810535
## Run 15 stress 0.1807829
## Run 16 stress 0.1837965
## Run 17 stress 0.1806178
## Run 18 stress 0.1829143
## Run 19 stress 0.1809495
## Run 20 stress 0.1811565
## Run 21 stress 0.1844161
## Run 22 stress 0.183545
## Run 23 stress 0.1805918
## Run 24 stress 0.1828067
## Run 25 stress 0.1807712
## Run 26 stress 0.1815542
## Run 27 stress 0.1808953
## Run 28 stress 0.1816259
## Run 29 stress 0.1825052
## Run 30 stress 0.1834418
## Run 31 stress 0.1813198
## Run 32 stress 0.1810033
## Run 33 stress 0.180476
## ... Procrustes: rmse 0.01603817 max resid 0.2297263
## Run 34 stress 0.1806056
## Run 35 stress 0.1836544
## Run 36 stress 0.1802247
## ... Procrustes: rmse 0.01256078 max resid 0.1888376
## Run 37 stress 0.1809401
## Run 38 stress 0.1819349
## Run 39 stress 0.1811702
## Run 40 stress 0.1818746
## Run 41 stress 0.1812262
## Run 42 stress 0.1805689
## Run 43 stress 0.1804361
## ... Procrustes: rmse 0.009543539 max resid 0.1151564
## Run 44 stress 0.1844368
## Run 45 stress 0.1805961
## Run 46 stress 0.1808972
## Run 47 stress 0.1822709
## Run 48 stress 0.1837717
## Run 49 stress 0.1819097
## Run 50 stress 0.1807142
## Run 51 stress 0.1804499
## ... Procrustes: rmse 0.02084664 max resid 0.3182816
## Run 52 stress 0.1805091
## ... Procrustes: rmse 0.02006514 max resid 0.3012419
## Run 53 stress 0.1805673
## Run 54 stress 0.183137
## Run 55 stress 0.183093
## Run 56 stress 0.1811575
## Run 57 stress 0.1806697
## Run 58 stress 0.1807773
## Run 59 stress 0.1826691
## Run 60 stress 0.1800559
## ... Procrustes: rmse 0.006578451 max resid 0.08435007
## Run 61 stress 0.1813459
## Run 62 stress 0.1809039
## Run 63 stress 0.1807454
## Run 64 stress 0.1834446
## Run 65 stress 0.1811507
## Run 66 stress 0.1813321
## Run 67 stress 0.1806274
## Run 68 stress 0.1803747
## ... Procrustes: rmse 0.006868104 max resid 0.06655475
## Run 69 stress 0.1832643
## Run 70 stress 0.1807037
## Run 71 stress 0.1807365
## Run 72 stress 0.180696
## Run 73 stress 0.1810531
## Run 74 stress 0.183301
## Run 75 stress 0.1816265
## Run 76 stress 0.1816314
## Run 77 stress 0.1809428
## Run 78 stress 0.1816406
## Run 79 stress 0.181438
## Run 80 stress 0.1802526
## ... Procrustes: rmse 0.006846106 max resid 0.04939419
## Run 81 stress 0.1807639
## Run 82 stress 0.1816743
## Run 83 stress 0.1835959
## Run 84 stress 0.1804778
## ... Procrustes: rmse 0.009564894 max resid 0.08641781
## Run 85 stress 0.1810681
## Run 86 stress 0.1803743
## ... Procrustes: rmse 0.01376968 max resid 0.2030871
## Run 87 stress 0.1821819
## Run 88 stress 0.1809071
## Run 89 stress 0.1827195
## Run 90 stress 0.1803422
## ... Procrustes: rmse 0.007660817 max resid 0.1087233
## Run 91 stress 0.1816065
## Run 92 stress 0.1817996
## Run 93 stress 0.1840305
## Run 94 stress 0.1805367
## Run 95 stress 0.1801433
## ... Procrustes: rmse 0.006559276 max resid 0.06968077
## Run 96 stress 0.1801789
## ... Procrustes: rmse 0.00681436 max resid 0.0749437
## Run 97 stress 0.1805464
## Run 98 stress 0.1802771
## ... Procrustes: rmse 0.00916249 max resid 0.1215026
## Run 99 stress 0.1819495
## Run 100 stress 0.1809188
## Run 101 stress 0.1841425
## Run 102 stress 0.180598
## Run 103 stress 0.180749
## Run 104 stress 0.1810347
## Run 105 stress 0.1804287
## ... Procrustes: rmse 0.01292494 max resid 0.1967408
## Run 106 stress 0.1817114
## Run 107 stress 0.1803097
## ... Procrustes: rmse 0.007336807 max resid 0.06503951
## Run 108 stress 0.1814069
## Run 109 stress 0.1809088
## Run 110 stress 0.1810316
## Run 111 stress 0.1830078
## Run 112 stress 0.18391
## Run 113 stress 0.1840016
## Run 114 stress 0.1806406
## Run 115 stress 0.1811548
## Run 116 stress 0.1819363
## Run 117 stress 0.1828125
## Run 118 stress 0.1806492
## Run 119 stress 0.1819042
## Run 120 stress 0.1804936
## ... Procrustes: rmse 0.01363679 max resid 0.1932652
## Run 121 stress 0.1828812
## Run 122 stress 0.180405
## ... Procrustes: rmse 0.01896052 max resid 0.2957022
## Run 123 stress 0.1841477
## Run 124 stress 0.1808911
## Run 125 stress 0.1805707
## Run 126 stress 0.1835465
## Run 127 stress 0.1824472
## Run 128 stress 0.1830995
## Run 129 stress 0.180322
## ... Procrustes: rmse 0.01047497 max resid 0.1237494
## Run 130 stress 0.180844
## Run 131 stress 0.180687
## Run 132 stress 0.1821559
## Run 133 stress 0.1827156
## Run 134 stress 0.1808858
## Run 135 stress 0.1803782
## ... Procrustes: rmse 0.009086889 max resid 0.0953232
## Run 136 stress 0.1804617
## ... Procrustes: rmse 0.01338299 max resid 0.2122864
## Run 137 stress 0.1835722
## Run 138 stress 0.1809247
## Run 139 stress 0.1811366
## Run 140 stress 0.1811599
## Run 141 stress 0.1823912
## Run 142 stress 0.1809011
## Run 143 stress 0.1838304
## Run 144 stress 0.1811361
## Run 145 stress 0.1814769
## Run 146 stress 0.1806402
## Run 147 stress 0.1826331
## Run 148 stress 0.1840076
## Run 149 stress 0.1808494
## Run 150 stress 0.1844777
## Run 151 stress 0.1803724
## ... Procrustes: rmse 0.009455823 max resid 0.1149696
## Run 152 stress 0.1810778
## Run 153 stress 0.1810257
## Run 154 stress 0.1806025
## Run 155 stress 0.180401
## ... Procrustes: rmse 0.007509136 max resid 0.06634573
## Run 156 stress 0.1816974
## Run 157 stress 0.1807964
## Run 158 stress 0.1807348
## Run 159 stress 0.1804161
## ... Procrustes: rmse 0.01267189 max resid 0.1870404
## Run 160 stress 0.1822351
## Run 161 stress 0.1820176
## Run 162 stress 0.1824289
## Run 163 stress 0.1834178
## Run 164 stress 0.1807519
## Run 165 stress 0.181611
## Run 166 stress 0.1806634
## Run 167 stress 0.1819039
## Run 168 stress 0.1810552
## Run 169 stress 0.1808497
## Run 170 stress 0.1812339
## Run 171 stress 0.1806493
## Run 172 stress 0.1844967
## Run 173 stress 0.1808117
## Run 174 stress 0.1802321
## ... Procrustes: rmse 0.01357134 max resid 0.2032147
## Run 175 stress 0.1824363
## Run 176 stress 0.1832015
## Run 177 stress 0.1826769
## Run 178 stress 0.182011
## Run 179 stress 0.1822027
## Run 180 stress 0.1825639
## Run 181 stress 0.1806176
## Run 182 stress 0.1803904
## ... Procrustes: rmse 0.008902766 max resid 0.06830609
## Run 183 stress 0.1805028
## ... Procrustes: rmse 0.01374823 max resid 0.2102734
## Run 184 stress 0.1814764
## Run 185 stress 0.1806635
## Run 186 stress 0.1814113
## Run 187 stress 0.1823876
## Run 188 stress 0.1826342
## Run 189 stress 0.1817857
## Run 190 stress 0.1828529
## Run 191 stress 0.1808799
## Run 192 stress 0.1806598
## Run 193 stress 0.1807154
## Run 194 stress 0.1820007
## Run 195 stress 0.1843896
## Run 196 stress 0.1821049
## Run 197 stress 0.1805543
## Run 198 stress 0.1818226
## Run 199 stress 0.180387
## ... Procrustes: rmse 0.00817496 max resid 0.1000537
## Run 200 stress 0.1807325
## *** Best solution was not repeated -- monoMDS stopping criteria:
## 198: no. of iterations >= maxit
## 2: stress ratio > sratmax#doesnt converge - whats up?
pk_NMDS##
## Call:
## metaMDS(comm = counts_wide_no0, trymax = 200)
##
## global Multidimensional Scaling using monoMDS
##
## Data: wisconsin(sqrt(counts_wide_no0))
## Distance: bray
##
## Dimensions: 2
## Stress: 0.1800154
## Stress type 1, weak ties
## Best solution was not repeated after 200 tries
## The best solution was from try 8 (random start)
## Scaling: centring, PC rotation, halfchange scaling
## Species: expanded scores based on 'wisconsin(sqrt(counts_wide_no0))'#stress (ideally <0.2)
stressplot(pk_NMDS)
plot(pk_NMDS)
plot_df <- scores(pk_NMDS, display = "sites") %>%
as.data.frame() %>%
rownames_to_column("site") %>%
full_join(meta_data_no0, by = c("site" = "sample_ID"))
#plot_df$Crop <- factor(plot_df$Crop, levels = c("Apple","Pear","Avocado","Kiwifruit","Pak Choi","Carrot", "Onion","Radish"))
plot_df$Crop <- factor(plot_df$Crop, levels = c("Avocado","Apple","Pear","Kiwifruit","Pak Choi","Radish", "Carrot","Onion"))
plot_nmds <- ggplot(plot_df, aes(x = NMDS1, y = NMDS2, color = Crop)) +
geom_point(size = 2, alpha = 0.8) +
stat_ellipse(linetype = 2, linewidth = 0.5) +
theme_bw()+
labs(title = "NMDS")
plot_nmds
Beta and MDS plot
this is a little arbitary but means the MDS plot isnt trying to converge stuff with basically no data
Eddy to look at pairwise.adonis for pairwise data (note that this seems to be a very different statistical test to what vegans::adonis2 does … need to read)
#################################################################
#setting a minimum count of 5 across at least 2 taxa#
#################################################################
#remove <5 counts:
counts_wide_no0 <- counts_wide[rowSums(counts_wide) >= 5, ]
#remove things that are only from a single species
counts_wide_no0 <-counts_wide_no0[which(rowSums(1*(counts_wide_no0!=0))>1),]
meta_data_no0<-meta_data %>% filter(sample_ID %in% row.names(counts_wide_no0))
#redoing the beta as well
#crop
perm<-adonis2(counts_wide_no0 ~ Crop,data=meta_data_no0)
perm## Permutation test for adonis under reduced model
## Permutation: free
## Number of permutations: 999
##
## adonis2(formula = counts_wide_no0 ~ Crop, data = meta_data_no0)
## Df SumOfSqs R2 F Pr(>F)
## Model 7 32.76 0.27146 16.448 0.001 ***
## Residual 309 87.92 0.72854
## Total 316 120.68 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1pairwise_adonis<-pairwise.adonis2(counts_wide_no0 ~ Crop, data = meta_data_no0)
pairwise_adonis## $parent_call
## [1] "counts_wide_no0 ~ Crop , strata = Null , permutations 999"
##
## $Apple_vs_Pear
## Df SumOfSqs R2 F Pr(>F)
## Model 1 0.8318 0.0885 2.6214 0.025 *
## Residual 27 8.5672 0.9115
## Total 28 9.3990 1.0000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $Apple_vs_Avocado
## Df SumOfSqs R2 F Pr(>F)
## Model 1 2.1888 0.09188 7.0824 0.001 ***
## Residual 70 21.6331 0.90812
## Total 71 23.8218 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $Apple_vs_Kiwifruit
## Df SumOfSqs R2 F Pr(>F)
## Model 1 1.7879 0.1133 5.3665 0.001 ***
## Residual 42 13.9925 0.8867
## Total 43 15.7803 1.0000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $`Apple_vs_Pak Choi`
## Df SumOfSqs R2 F Pr(>F)
## Model 1 2.5589 0.13831 7.8648 0.001 ***
## Residual 49 15.9426 0.86169
## Total 50 18.5015 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $Apple_vs_Radish
## Df SumOfSqs R2 F Pr(>F)
## Model 1 1.3709 0.17073 3.9116 0.001 ***
## Residual 19 6.6587 0.82927
## Total 20 8.0295 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $Apple_vs_Onion
## Df SumOfSqs R2 F Pr(>F)
## Model 1 2.8667 0.15224 10.236 0.001 ***
## Residual 57 15.9637 0.84776
## Total 58 18.8304 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $Apple_vs_Carrot
## Df SumOfSqs R2 F Pr(>F)
## Model 1 3.1209 0.11142 12.414 0.001 ***
## Residual 99 24.8886 0.88858
## Total 100 28.0095 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $Pear_vs_Avocado
## Df SumOfSqs R2 F Pr(>F)
## Model 1 2.1075 0.0819 7.0474 0.001 ***
## Residual 79 23.6247 0.9181
## Total 80 25.7322 1.0000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $Pear_vs_Kiwifruit
## Df SumOfSqs R2 F Pr(>F)
## Model 1 3.1454 0.16443 10.036 0.001 ***
## Residual 51 15.9841 0.83557
## Total 52 19.1295 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $`Pear_vs_Pak Choi`
## Df SumOfSqs R2 F Pr(>F)
## Model 1 4.3787 0.19624 14.161 0.001 ***
## Residual 58 17.9342 0.80376
## Total 59 22.3129 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $Pear_vs_Radish
## Df SumOfSqs R2 F Pr(>F)
## Model 1 1.9700 0.1855 6.3767 0.001 ***
## Residual 28 8.6503 0.8145
## Total 29 10.6204 1.0000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $Pear_vs_Onion
## Df SumOfSqs R2 F Pr(>F)
## Model 1 4.6645 0.20621 17.146 0.001 ***
## Residual 66 17.9554 0.79379
## Total 67 22.6199 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $Pear_vs_Carrot
## Df SumOfSqs R2 F Pr(>F)
## Model 1 4.904 0.1543 19.705 0.001 ***
## Residual 108 26.880 0.8457
## Total 109 31.785 1.0000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $Avocado_vs_Kiwifruit
## Df SumOfSqs R2 F Pr(>F)
## Model 1 4.209 0.12655 13.619 0.001 ***
## Residual 94 29.050 0.87345
## Total 95 33.259 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $`Avocado_vs_Pak Choi`
## Df SumOfSqs R2 F Pr(>F)
## Model 1 7.603 0.19696 24.772 0.001 ***
## Residual 101 31.000 0.80304
## Total 102 38.603 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $Avocado_vs_Radish
## Df SumOfSqs R2 F Pr(>F)
## Model 1 2.799 0.11417 9.1512 0.001 ***
## Residual 71 21.716 0.88583
## Total 72 24.515 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $Avocado_vs_Onion
## Df SumOfSqs R2 F Pr(>F)
## Model 1 8.804 0.22106 30.934 0.001 ***
## Residual 109 31.021 0.77894
## Total 110 39.825 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $Avocado_vs_Carrot
## Df SumOfSqs R2 F Pr(>F)
## Model 1 10.482 0.20786 39.622 0.001 ***
## Residual 151 39.946 0.79214
## Total 152 50.428 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $`Kiwifruit_vs_Pak Choi`
## Df SumOfSqs R2 F Pr(>F)
## Model 1 5.3218 0.18555 16.631 0.001 ***
## Residual 73 23.3595 0.81445
## Total 74 28.6813 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $Kiwifruit_vs_Radish
## Df SumOfSqs R2 F Pr(>F)
## Model 1 1.6203 0.10323 4.9499 0.001 ***
## Residual 43 14.0756 0.89677
## Total 44 15.6959 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $Kiwifruit_vs_Onion
## Df SumOfSqs R2 F Pr(>F)
## Model 1 6.6986 0.2227 23.207 0.001 ***
## Residual 81 23.3806 0.7773
## Total 82 30.0792 1.0000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $Kiwifruit_vs_Carrot
## Df SumOfSqs R2 F Pr(>F)
## Model 1 8.759 0.2133 33.35 0.001 ***
## Residual 123 32.305 0.7867
## Total 124 41.065 1.0000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $`Pak Choi_vs_Radish`
## Df SumOfSqs R2 F Pr(>F)
## Model 1 0.7268 0.04338 2.2675 0.012 *
## Residual 50 16.0257 0.95662
## Total 51 16.7524 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $`Pak Choi_vs_Onion`
## Df SumOfSqs R2 F Pr(>F)
## Model 1 3.5642 0.12335 12.382 0.001 ***
## Residual 88 25.3307 0.87665
## Total 89 28.8950 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $`Pak Choi_vs_Carrot`
## Df SumOfSqs R2 F Pr(>F)
## Model 1 5.785 0.14449 21.956 0.001 ***
## Residual 130 34.256 0.85551
## Total 131 40.041 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $Radish_vs_Onion
## Df SumOfSqs R2 F Pr(>F)
## Model 1 1.6844 0.095 6.0881 0.001 ***
## Residual 58 16.0468 0.905
## Total 59 17.7312 1.000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $Radish_vs_Carrot
## Df SumOfSqs R2 F Pr(>F)
## Model 1 2.043 0.07562 8.1811 0.001 ***
## Residual 100 24.972 0.92438
## Total 101 27.015 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $Onion_vs_Carrot
## Df SumOfSqs R2 F Pr(>F)
## Model 1 3.128 0.08362 12.592 0.001 ***
## Residual 138 34.277 0.91638
## Total 139 37.404 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## attr(,"class")
## [1] "pwadstrata" "list"tests<-names(pairwise_adonis)
tests<-tests[-1]
columns= c("Test","pvalue")
myData = data.frame(matrix(nrow = 0, ncol = length(columns)))
for (item in tests){
# print(item)
pvalue<-eval(parse(text =paste0('pairwise_adonis$','`',item,'`' ,'$`Pr(>F)`[1]')))
# print(pvalue)
out<-data.frame(Test = item, pvalue = pvalue)
#print(out)
myData<-rbind(myData,out)
}
write.csv(myData,'Pairwise_adonis_beta_diversity_allreplicates_countsvscrops_mincount5.csv',quote = F,row.names = F)
#flowertime
perm<-adonis2(counts_wide_no0 ~ flowertime,data=meta_data_no0)
perm## Permutation test for adonis under reduced model
## Permutation: free
## Number of permutations: 999
##
## adonis2(formula = counts_wide_no0 ~ flowertime, data = meta_data_no0)
## Df SumOfSqs R2 F Pr(>F)
## Model 1 15.008 0.12436 44.737 0.001 ***
## Residual 315 105.672 0.87564
## Total 316 120.679 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#pairwise_adonis<-pairwise.adonis2(counts_wide_no0 ~ flowertime, data = meta_data_no0)
#pairwise_adonis
#I think to look at the impact of region within a crop we need to run this within a crop. This is because there is little overlap in regions between crops and the 0,0 situation makes it too confusing (at least for me)
#so just testing does community composition vary between regions within a crop.
categories_crop <- meta_data %>% filter(Crop!='Radish') %>% select(Crop) %>% unique()
categories_crop$Crop <- gsub("Pak Choi", "Pakchoi", categories_crop$Crop)
for (item in categories_crop$Crop){
print(paste('Analysing',item))
filtered_counts_wide_no0 <- counts_wide_no0[grepl(paste0('^',item), rownames(counts_wide_no0)), ] %>% select(where(~ sum(.) != 0))
filtered_meta_data_no0<-meta_data %>% filter(sample_ID %in% row.names(filtered_counts_wide_no0))
perm<-adonis2(filtered_counts_wide_no0 ~ Region,data=filtered_meta_data_no0)
print(perm)
pairwise_adonis<-pairwise.adonis2(filtered_counts_wide_no0 ~ Region, data = filtered_meta_data_no0)
print(pairwise_adonis)
tests<-names(pairwise_adonis)
tests<-tests[-1]
columns= c("Test","pvalue")
myData = data.frame(matrix(nrow = 0, ncol = length(columns)))
for (item1 in tests){
# print(item)
pvalue<-eval(parse(text =paste0('pairwise_adonis$','`',item1,'`' ,'$`Pr(>F)`[1]')))
# print(pvalue)
out<-data.frame(Test = item1, pvalue = pvalue)
#print(out)
myData<-rbind(myData,out)
}
write.csv(myData,paste0('Pairwise_adonis_beta_diversity_allreplicates_regionvs',item,'_mincount5.csv'),quote = F,row.names = F)
}## [1] "Analysing Apple"
## Permutation test for adonis under reduced model
## Permutation: free
## Number of permutations: 999
##
## adonis2(formula = filtered_counts_wide_no0 ~ Region, data = filtered_meta_data_no0)
## Df SumOfSqs R2 F Pr(>F)
## Model 1 0.8974 0.27295 3.0034 0.025 *
## Residual 8 2.3904 0.72705
## Total 9 3.2878 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## $parent_call
## [1] "filtered_counts_wide_no0 ~ Region , strata = Null , permutations 999"
##
## $`Hawkes Bay_vs_Tasman`
## Df SumOfSqs R2 F Pr(>F)
## Model 1 0.8974 0.27295 3.0034 0.023 *
## Residual 8 2.3904 0.72705
## Total 9 3.2878 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## attr(,"class")
## [1] "pwadstrata" "list"
## [1] "Analysing Pear"
## Permutation test for adonis under reduced model
## Permutation: free
## Number of permutations: 999
##
## adonis2(formula = filtered_counts_wide_no0 ~ Region, data = filtered_meta_data_no0)
## Df SumOfSqs R2 F Pr(>F)
## Model 1 2.0378 0.38599 10.687 0.001 ***
## Residual 17 3.2416 0.61401
## Total 18 5.2794 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## $parent_call
## [1] "filtered_counts_wide_no0 ~ Region , strata = Null , permutations 999"
##
## $`Hawkes Bay_vs_Tasman`
## Df SumOfSqs R2 F Pr(>F)
## Model 1 2.0378 0.38599 10.687 0.001 ***
## Residual 17 3.2416 0.61401
## Total 18 5.2794 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## attr(,"class")
## [1] "pwadstrata" "list"
## [1] "Analysing Avocado"
## Permutation test for adonis under reduced model
## Permutation: free
## Number of permutations: 999
##
## adonis2(formula = filtered_counts_wide_no0 ~ Region, data = filtered_meta_data_no0)
## Df SumOfSqs R2 F Pr(>F)
## Model 2 1.9528 0.10645 3.5144 0.001 ***
## Residual 59 16.3924 0.89355
## Total 61 18.3453 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## $parent_call
## [1] "filtered_counts_wide_no0 ~ Region , strata = Null , permutations 999"
##
## $`Bay of Plenty_vs_Northland`
## Df SumOfSqs R2 F Pr(>F)
## Model 1 1.6054 0.09541 5.6954 0.001 ***
## Residual 54 15.2215 0.90459
## Total 55 16.8269 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $`Bay of Plenty_vs_Whangarei`
## Df SumOfSqs R2 F Pr(>F)
## Model 1 0.3023 0.02304 1.0377 0.384
## Residual 44 12.8187 0.97696
## Total 45 13.1210 1.00000
##
## $Northland_vs_Whangarei
## Df SumOfSqs R2 F Pr(>F)
## Model 1 0.7848 0.14193 3.308 0.002 **
## Residual 20 4.7447 0.85807
## Total 21 5.5295 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## attr(,"class")
## [1] "pwadstrata" "list"
## [1] "Analysing Kiwifruit"
## Permutation test for adonis under reduced model
## Permutation: free
## Number of permutations: 999
##
## adonis2(formula = filtered_counts_wide_no0 ~ Region, data = filtered_meta_data_no0)
## Df SumOfSqs R2 F Pr(>F)
## Model 3 2.2596 0.21109 2.6757 0.001 ***
## Residual 30 8.4450 0.78891
## Total 33 10.7047 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## 'nperm' >= set of all permutations: complete enumeration.## Set of permutations < 'minperm'. Generating entire set.## $parent_call
## [1] "filtered_counts_wide_no0 ~ Region , strata = Null , permutations 999"
##
## $`Bay of Plenty_vs_Poverty Bay`
## Df SumOfSqs R2 F Pr(>F)
## Model 1 0.1740 0.02843 0.5561 0.853
## Residual 19 5.9439 0.97157
## Total 20 6.1178 1.00000
##
## $`Bay of Plenty_vs_Hawkes Bay`
## Df SumOfSqs R2 F Pr(>F)
## Model 1 0.5780 0.08791 1.8312 0.076 .
## Residual 19 5.9973 0.91209
## Total 20 6.5754 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $`Bay of Plenty_vs_Tasman`
## Df SumOfSqs R2 F Pr(>F)
## Model 1 1.6250 0.17188 5.8116 0.001 ***
## Residual 28 7.8291 0.82812
## Total 29 9.4540 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $`Poverty Bay_vs_Hawkes Bay`
## Df SumOfSqs R2 F Pr(>F)
## Model 1 0.35776 0.36742 1.1616 0.6667
## Residual 2 0.61596 0.63258
## Total 3 0.97372 1.00000
##
## $`Poverty Bay_vs_Tasman`
## Df SumOfSqs R2 F Pr(>F)
## Model 1 0.52261 0.17595 2.3486 0.049 *
## Residual 11 2.44769 0.82405
## Total 12 2.97030 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $`Hawkes Bay_vs_Tasman`
## Df SumOfSqs R2 F Pr(>F)
## Model 1 0.31247 0.11106 1.3742 0.19
## Residual 11 2.50115 0.88894
## Total 12 2.81362 1.00000
##
## attr(,"class")
## [1] "pwadstrata" "list"
## [1] "Analysing Pakchoi"
## Permutation test for adonis under reduced model
## Permutation: free
## Number of permutations: 999
##
## adonis2(formula = filtered_counts_wide_no0 ~ Region, data = filtered_meta_data_no0)
## Df SumOfSqs R2 F Pr(>F)
## Model 3 2.5275 0.19973 3.0781 0.001 ***
## Residual 37 10.1273 0.80027
## Total 40 12.6548 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## Set of permutations < 'minperm'. Generating entire set.## $parent_call
## [1] "filtered_counts_wide_no0 ~ Region , strata = Null , permutations 999"
##
## $Auckland_vs_Canterbury
## Df SumOfSqs R2 F Pr(>F)
## Model 1 1.1250 0.13494 4.2115 0.001 ***
## Residual 27 7.2122 0.86506
## Total 28 8.3372 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $`Auckland_vs_Hawkes Bay`
## Df SumOfSqs R2 F Pr(>F)
## Model 1 0.3954 0.22901 1.4852 0.23
## Residual 5 1.3312 0.77099
## Total 6 1.7265 1.00000
##
## $Auckland_vs_Southland
## Df SumOfSqs R2 F Pr(>F)
## Model 1 0.9536 0.22868 3.2612 0.004 **
## Residual 11 3.2165 0.77132
## Total 12 4.1702 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $`Canterbury_vs_Hawkes Bay`
## Df SumOfSqs R2 F Pr(>F)
## Model 1 0.5671 0.07584 2.1335 0.025 *
## Residual 26 6.9107 0.92416
## Total 27 7.4778 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $Canterbury_vs_Southland
## Df SumOfSqs R2 F Pr(>F)
## Model 1 0.9286 0.09549 3.3781 0.001 ***
## Residual 32 8.7961 0.90451
## Total 33 9.7247 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $`Hawkes Bay_vs_Southland`
## Df SumOfSqs R2 F Pr(>F)
## Model 1 0.6992 0.19346 2.3987 0.039 *
## Residual 10 2.9150 0.80654
## Total 11 3.6142 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## attr(,"class")
## [1] "pwadstrata" "list"
## [1] "Analysing Onion"
## Permutation test for adonis under reduced model
## Permutation: free
## Number of permutations: 999
##
## adonis2(formula = filtered_counts_wide_no0 ~ Region, data = filtered_meta_data_no0)
## Df SumOfSqs R2 F Pr(>F)
## Model 3 1.6258 0.12826 2.2069 0.001 ***
## Residual 45 11.0501 0.87174
## Total 48 12.6759 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## $parent_call
## [1] "filtered_counts_wide_no0 ~ Region , strata = Null , permutations 999"
##
## $Canterbury_vs_Otago
## Df SumOfSqs R2 F Pr(>F)
## Model 1 0.4072 0.09027 1.786 0.04 *
## Residual 18 4.1038 0.90973
## Total 19 4.5110 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $Canterbury_vs_Marlborough
## Df SumOfSqs R2 F Pr(>F)
## Model 1 0.6737 0.07373 2.7063 0.008 **
## Residual 34 8.4640 0.92627
## Total 35 9.1378 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $Canterbury_vs_Wairarapa
## Df SumOfSqs R2 F Pr(>F)
## Model 1 0.5556 0.10107 2.3612 0.017 *
## Residual 21 4.9415 0.89893
## Total 22 5.4971 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $Otago_vs_Marlborough
## Df SumOfSqs R2 F Pr(>F)
## Model 1 0.3443 0.05336 1.3528 0.185
## Residual 24 6.1087 0.94664
## Total 25 6.4530 1.00000
##
## $Otago_vs_Wairarapa
## Df SumOfSqs R2 F Pr(>F)
## Model 1 0.42663 0.14161 1.8147 0.073 .
## Residual 11 2.58610 0.85839
## Total 12 3.01273 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $Marlborough_vs_Wairarapa
## Df SumOfSqs R2 F Pr(>F)
## Model 1 0.6893 0.09027 2.6792 0.007 **
## Residual 27 6.9463 0.90973
## Total 28 7.6356 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## attr(,"class")
## [1] "pwadstrata" "list"
## [1] "Analysing Carrot"
## Permutation test for adonis under reduced model
## Permutation: free
## Number of permutations: 999
##
## adonis2(formula = filtered_counts_wide_no0 ~ Region, data = filtered_meta_data_no0)
## Df SumOfSqs R2 F Pr(>F)
## Model 1 0.4846 0.02243 2.0424 0.026 *
## Residual 89 21.1162 0.97757
## Total 90 21.6008 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## $parent_call
## [1] "filtered_counts_wide_no0 ~ Region , strata = Null , permutations 999"
##
## $Canterbury_vs_Otago
## Df SumOfSqs R2 F Pr(>F)
## Model 1 0.4846 0.02243 2.0424 0.026 *
## Residual 89 21.1162 0.97757
## Total 90 21.6008 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## attr(,"class")
## [1] "pwadstrata" "list"#MDS
pk_NMDS <- metaMDS(counts_wide_no0,trymax=200)## Square root transformation
## Wisconsin double standardization
## Run 0 stress 0.2400227
## Run 1 stress 0.239265
## ... New best solution
## ... Procrustes: rmse 0.009357989 max resid 0.1174194
## Run 2 stress 0.2392672
## ... Procrustes: rmse 0.0006387654 max resid 0.004872033
## ... Similar to previous best
## Run 3 stress 0.2516446
## Run 4 stress 0.266451
## Run 5 stress 0.2397358
## ... Procrustes: rmse 0.009537836 max resid 0.1081885
## Run 6 stress 0.2392506
## ... New best solution
## ... Procrustes: rmse 0.001149388 max resid 0.0133466
## Run 7 stress 0.2388986
## ... New best solution
## ... Procrustes: rmse 0.006357251 max resid 0.1082031
## Run 8 stress 0.2389823
## ... Procrustes: rmse 0.007794151 max resid 0.1081838
## Run 9 stress 0.2389776
## ... Procrustes: rmse 0.007958551 max resid 0.108157
## Run 10 stress 0.2387175
## ... New best solution
## ... Procrustes: rmse 0.004613259 max resid 0.07844746
## Run 11 stress 0.2658607
## Run 12 stress 0.264785
## Run 13 stress 0.2400242
## Run 14 stress 0.2400349
## Run 15 stress 0.238737
## ... Procrustes: rmse 0.0006361614 max resid 0.01008383
## Run 16 stress 0.2406161
## Run 17 stress 0.2389822
## ... Procrustes: rmse 0.006174256 max resid 0.1084564
## Run 18 stress 0.2389818
## ... Procrustes: rmse 0.006725444 max resid 0.1083825
## Run 19 stress 0.2392642
## Run 20 stress 0.2389683
## ... Procrustes: rmse 0.006274827 max resid 0.108424
## Run 21 stress 0.2392678
## Run 22 stress 0.2700179
## Run 23 stress 0.2396245
## Run 24 stress 0.2387175
## ... Procrustes: rmse 0.0002217419 max resid 0.002766253
## ... Similar to previous best
## *** Best solution repeated 1 times#converges - yay - a little stressed but is a lot of data
pk_NMDS##
## Call:
## metaMDS(comm = counts_wide_no0, trymax = 200)
##
## global Multidimensional Scaling using monoMDS
##
## Data: wisconsin(sqrt(counts_wide_no0))
## Distance: bray
##
## Dimensions: 2
## Stress: 0.2387175
## Stress type 1, weak ties
## Best solution was repeated 1 time in 24 tries
## The best solution was from try 10 (random start)
## Scaling: centring, PC rotation, halfchange scaling
## Species: expanded scores based on 'wisconsin(sqrt(counts_wide_no0))'#stress
stressplot(pk_NMDS)
plot(pk_NMDS)
plot_df <- scores(pk_NMDS, display = "sites") %>%
as.data.frame() %>%
rownames_to_column("site") %>%
full_join(meta_data_no0, by = c("site" = "sample_ID"))
#plot_df$Crop <- factor(plot_df$Crop, levels = c("Apple","Pear","Avocado","Kiwifruit","Pak Choi","Carrot", "Onion","Radish"))
plot_df$Crop <- factor(plot_df$Crop, levels = c("Avocado","Apple","Pear","Kiwifruit","Pak Choi","Radish", "Carrot","Onion"))
plot_nmds <- ggplot(plot_df, aes(x = NMDS1, y = NMDS2, color = Crop)) +
geom_point(size = 2, alpha = 1) +
stat_ellipse(linetype = 2, linewidth = 0.5) +
theme_bw()+
scale_colour_manual(values=col_bound)+
labs(title = "NMDS")
plot_nmds
plot_nmds <- ggplot(plot_df, aes(x = NMDS1, y = NMDS2, color = Crop,shape=Crop)) +
geom_point(size = 3, alpha = 1) +
scale_shape_manual(values = c("Avocado" = 15, "Apple" = 15, "Pear" = 15, "Kiwifruit" = 15, "Pak Choi" = 16, "Radish" = 16, "Carrot" = 16, "Onion" = 16))+
stat_ellipse(linetype = 2, linewidth = 0.5) +
theme_bw()+
scale_colour_manual(values=col_bound)+
labs(title = "NMDS")
plot_nmds
plot_nmds <- ggplot(plot_df, aes(x = NMDS1, y = NMDS2, color = Crop,shape=Crop)) +
geom_point(size = 2, alpha = 1) +
scale_shape_manual(values = c("Avocado" = 16, "Apple" = 16, "Pear" = 16, "Kiwifruit" = 16, "Pak Choi" = 15, "Radish" = 15, "Carrot" = 15, "Onion" = 15))+
stat_ellipse(linetype = 1, linewidth = 0.5) +
theme_bw()+
scale_colour_manual(values=col_bound)+
labs(title = "NMDS")
plot_nmds
# If you set the min at 6 it drops that outlier
counts_wide_no0 <- counts_wide[rowSums(counts_wide) >= 6, ]
#remove things that are only from a single species
counts_wide_no0 <-counts_wide_no0[which(rowSums(1*(counts_wide_no0!=0))>1),]
meta_data_no0<-meta_data %>% filter(sample_ID %in% row.names(counts_wide_no0))
#MDS
pk_NMDS <- metaMDS(counts_wide_no0,trymax=200)## Square root transformation
## Wisconsin double standardization
## Run 0 stress 0.2381988
## Run 1 stress 0.2367926
## ... New best solution
## ... Procrustes: rmse 0.01308996 max resid 0.1264205
## Run 2 stress 0.2438725
## Run 3 stress 0.2449335
## Run 4 stress 0.2528548
## Run 5 stress 0.2415683
## Run 6 stress 0.237678
## Run 7 stress 0.2364148
## ... New best solution
## ... Procrustes: rmse 0.006873202 max resid 0.1168836
## Run 8 stress 0.2367782
## ... Procrustes: rmse 0.006820162 max resid 0.116753
## Run 9 stress 0.2400834
## Run 10 stress 0.239276
## Run 11 stress 0.2385495
## Run 12 stress 0.2369731
## Run 13 stress 0.2407344
## Run 14 stress 0.2369696
## Run 15 stress 0.2552951
## Run 16 stress 0.23704
## Run 17 stress 0.2367793
## ... Procrustes: rmse 0.006836271 max resid 0.1167659
## Run 18 stress 0.2364615
## ... Procrustes: rmse 0.005007343 max resid 0.08420072
## Run 19 stress 0.2367787
## ... Procrustes: rmse 0.006832405 max resid 0.11677
## Run 20 stress 0.2367931
## ... Procrustes: rmse 0.006881784 max resid 0.1167787
## Run 21 stress 0.2367785
## ... Procrustes: rmse 0.006832947 max resid 0.1167458
## Run 22 stress 0.2390624
## Run 23 stress 0.2630538
## Run 24 stress 0.2369718
## Run 25 stress 0.2519962
## Run 26 stress 0.2367925
## ... Procrustes: rmse 0.006875294 max resid 0.1167854
## Run 27 stress 0.2684408
## Run 28 stress 0.2394014
## Run 29 stress 0.2369713
## Run 30 stress 0.2369701
## Run 31 stress 0.2641893
## Run 32 stress 0.2405361
## Run 33 stress 0.2640991
## Run 34 stress 0.2370671
## Run 35 stress 0.2532711
## Run 36 stress 0.2369773
## Run 37 stress 0.2393817
## Run 38 stress 0.2708643
## Run 39 stress 0.2367926
## ... Procrustes: rmse 0.006879341 max resid 0.1167896
## Run 40 stress 0.2491148
## Run 41 stress 0.2370465
## Run 42 stress 0.2369703
## Run 43 stress 0.2393935
## Run 44 stress 0.2396342
## Run 45 stress 0.239866
## Run 46 stress 0.2364144
## ... New best solution
## ... Procrustes: rmse 0.0006114514 max resid 0.006484654
## ... Similar to previous best
## *** Best solution repeated 1 times#converges - yay - a little stressed but is a lot of data
pk_NMDS##
## Call:
## metaMDS(comm = counts_wide_no0, trymax = 200)
##
## global Multidimensional Scaling using monoMDS
##
## Data: wisconsin(sqrt(counts_wide_no0))
## Distance: bray
##
## Dimensions: 2
## Stress: 0.2364144
## Stress type 1, weak ties
## Best solution was repeated 1 time in 46 tries
## The best solution was from try 46 (random start)
## Scaling: centring, PC rotation, halfchange scaling
## Species: expanded scores based on 'wisconsin(sqrt(counts_wide_no0))'#stress
stressplot(pk_NMDS)
plot(pk_NMDS)
plot_df <- scores(pk_NMDS, display = "sites") %>%
as.data.frame() %>%
rownames_to_column("site") %>%
full_join(meta_data_no0, by = c("site" = "sample_ID"))
#plot_df$Crop <- factor(plot_df$Crop, levels = c("Apple","Pear","Avocado","Kiwifruit","Pak Choi","Carrot", "Onion","Radish"))
plot_df$Crop <- factor(plot_df$Crop, levels = c("Avocado","Apple","Pear","Kiwifruit","Pak Choi","Radish", "Carrot","Onion"))
plot_nmds <- ggplot(plot_df, aes(x = NMDS1, y = NMDS2, color = Crop)) +
geom_point(size = 2, alpha = 1) +
stat_ellipse(linetype = 2, linewidth = 0.5) +
theme_bw()+
scale_colour_manual(values=col_bound)+
labs(title = "NMDS")
plot_nmds## Warning in MASS::cov.trob(data[, vars]): Probable convergence failure
plot_nmds <- ggplot(plot_df, aes(x = NMDS1, y = NMDS2, color = Crop,shape=Crop)) +
geom_point(size = 3, alpha = 1) +
scale_shape_manual(values = c("Avocado" = 15, "Apple" = 15, "Pear" = 15, "Kiwifruit" = 15, "Pak Choi" = 16, "Radish" = 16, "Carrot" = 16, "Onion" = 16))+
stat_ellipse(linetype = 2, linewidth = 0.5) +
theme_bw()+
scale_colour_manual(values=col_bound)+
labs(title = "NMDS")
plot_nmds## Warning in MASS::cov.trob(data[, vars]): Probable convergence failure
plot_nmds <- ggplot(plot_df, aes(x = NMDS1, y = NMDS2, color = Crop,shape=Crop)) +
geom_point(size = 2, alpha = 1) +
scale_shape_manual(values = c("Avocado" = 16, "Apple" = 16, "Pear" = 16, "Kiwifruit" = 16, "Pak Choi" = 15, "Radish" = 15, "Carrot" = 15, "Onion" = 15))+
stat_ellipse(linetype = 1, linewidth = 0.5) +
theme_bw()+
scale_colour_manual(values=col_bound)+
labs(title = "NMDS")
plot_nmds## Warning in MASS::cov.trob(data[, vars]): Probable convergence failure
Inext
I next is designed to be robust to sampling so probably is very appropriate for this dataset
########
#innext#
########
library(iNEXT.3D)
#generate a list of the unique sample locations
categories <- unique(meta_data$Crop)
categories## [1] "Apple" "Pear" "Avocado" "Kiwifruit" "Pak Choi" "Radish"
## [7] "Onion" "Carrot"#create empty list
split_list <- list()
#fill empty list with a presence absence OTU from each location
head(allcrops_per_r_fly)## # A tibble: 6 × 7
## sample_ID Ordering Region `Location and season` Day Crop raw_count
## <chr> <fct> <chr> <chr> <chr> <chr> <dbl>
## 1 Apple_Hawkes Bay_… 32 Hawke… 104 Moteo-Pa road _2… Day_1 Apple 2
## 2 Apple_Hawkes Bay_… 33 Hawke… 104 Moteo-Pa road _2… Day_1 Apple 1
## 3 Apple_Hawkes Bay_… 34 Hawke… 104 Moteo-Pa road _2… Day_1 Apple 11
## 4 Apple_Hawkes Bay_… 54 Hawke… 104 Moteo-Pa road _2… Day_1 Apple 1
## 5 Apple_Hawkes Bay_… 34 Hawke… 104 Moteo-Pa road _2… Day_3 Apple 11
## 6 Apple_Hawkes Bay_… 14 Hawke… 185 Lawn road, Hasti… Day_1 Apple 1#I need to fill up the NA's for the samples with 0 flies with some dummy 0 for ones species to keep them in the next stage - so this is just to add a 0 value in and I've chosed the insect '27' at random (cause i know it occurs in the dataset so Im not adding something not present)
#in '' as its a factor not a number
allcrops_per_r_fly_innext<-allcrops_per_r_fly
allcrops_per_r_fly_innext$Ordering[is.na(allcrops_per_r_fly_innext$Ordering)] <- '27'
allcrops_per_r_fly_innext$raw_count[is.na(allcrops_per_r_fly_innext$raw_count)] <- 0
#create empty list
split_list <- list()
for (category in categories) {
print(category)
subset_data <- allcrops_per_r_fly_innext %>% filter(Crop == category)
# subset_data <- allcrops_per_r_fly_innext %>% filter(Crop == "Carrot")
test<-subset_data %>% ungroup() %>% dplyr::select(sample_ID,Ordering,raw_count)
test <- test %>% spread(key = sample_ID, value = raw_count)
test<-test %>% remove_rownames %>% column_to_rownames(var="Ordering")
test[is.na(test)] <- 0
# test <- test[rowSums(test == 0) != ncol(test), ]
test<-test %>% mutate(across(everything(), ~ ifelse(. > 0, 1, 0)))
split_list[[category]]<-as(test,'matrix')
} ## [1] "Apple"
## [1] "Pear"
## [1] "Avocado"
## [1] "Kiwifruit"
## [1] "Pak Choi"
## [1] "Radish"
## [1] "Onion"
## [1] "Carrot"matrix_list <- list(data = list())
for (category in categories) {
otu_table <- as(split_list[[category]], "matrix")
matrix_list[["data"]][[category]] <- otu_table
}
#here Im setting the endpoint as NULL which is double the input sample size (this is probably most robust)
out.raw <- iNEXT3D(data = matrix_list$data, diversity = 'TD', q = c(0, 1, 2), datatype = 'incidence_raw', nboot = 50,endpoint=NULL)
#there is various ways to look at these plots:
#within a crop
ggiNEXT3D(out.raw, type = 1, facet.var = 'Assemblage') + facet_wrap(~Assemblage, nrow = 3)
#across crops
ggiNEXT3D(out.raw, type = 1, facet.var = "Order.q")## Warning: The shape palette can deal with a maximum of 6 discrete values because more
## than 6 becomes difficult to discriminate
## ℹ you have requested 8 values. Consider specifying shapes manually if you need
## that many of them.## Warning: Removed 6 rows containing missing values or values outside the scale range
## (`geom_point()`).
#sample completeness
ggiNEXT3D(out.raw, type = 2, facet.var = "Order.q", color.var = "Assemblage")## Warning: The shape palette can deal with a maximum of 6 discrete values because more
## than 6 becomes difficult to discriminate
## ℹ you have requested 8 values. Consider specifying shapes manually if you need
## that many of them.## Warning: Removed 2 rows containing missing values or values outside the scale range
## (`geom_point()`).
#running innext as abundance as well
#create empty list
split_list <- list()
for (category in categories) {
print(category)
subset_data <- allcrops_per_r_fly_innext %>% filter(Crop == category)
# subset_data <- allcrops_per_r_fly_innext %>% filter(Crop == "Carrot")
test<-subset_data %>% ungroup() %>% dplyr::select(sample_ID,Ordering,raw_count)
test <- test %>% spread(key = sample_ID, value = raw_count)
test<-test %>% remove_rownames %>% column_to_rownames(var="Ordering")
test[is.na(test)] <- 0
test<-as.data.frame(rowSums(test))
colnames(test) <- c(paste0(category))
# test <- test[rowSums(test == 0) != ncol(test), ]
# test<-test %>% mutate(across(everything(), ~ ifelse(. > 0, 1, 0)))
split_list[[category]]<-as(test,'matrix')
} ## [1] "Apple"
## [1] "Pear"
## [1] "Avocado"
## [1] "Kiwifruit"
## [1] "Pak Choi"
## [1] "Radish"
## [1] "Onion"
## [1] "Carrot"matrix_list <- list(data = list())
for (category in categories) {
otu_table <- as(split_list[[category]], "matrix")
matrix_list[["data"]][[category]] <- otu_table
}
#here Im setting the endpoint as NULL which is double the input sample size (this is probably most robust)
out.raw <- iNEXT3D(data = matrix_list$data, diversity = 'TD', q = c(0, 1, 2), datatype = 'abundance', nboot = 50,endpoint=NULL)
#there is various ways to look at these plots:
#within a sample
ggiNEXT3D(out.raw, type = 1, facet.var = 'Assemblage') + facet_wrap(~Assemblage, nrow = 3)
#across samples
ggiNEXT3D(out.raw, type = 1, facet.var = "Order.q")## Warning: The shape palette can deal with a maximum of 6 discrete values because more
## than 6 becomes difficult to discriminate
## ℹ you have requested 8 values. Consider specifying shapes manually if you need
## that many of them.## Warning: Removed 6 rows containing missing values or values outside the scale range
## (`geom_point()`).
#yes well I think we can agree across samples works better for apple and pear
#but its interesting that avocado diversity depending on how you look at it can change quite a bitDoing analyses just on Day 1 samples to avoid the issue on uneven replication
I think this is probably the most conservative solution to this problem but Im happy to take other ideas
######################################################################
#conservatively taking only Day 1 samples and analysing
######################################################################
#conservatively if we just look at the first day due to all the biases in the data
meta_data_day1<-meta_data %>% filter(Day=='Day_1')
counts_day1<-allcrops_per_r_fly %>% ungroup() %>% filter(Day=='Day_1') %>% select(sample_ID,Ordering,raw_count)
#I need to fill up the NA's for the samples with 0 flies with some dummy 0 for ones species to keep them in the next stage - so this is just to add a 0 value in and I've chosed the insect '27' at random (cause i know it occurs in the dataset so Im not adding something not present)
#in '' as its a factor not a number
counts_day1$Ordering[is.na(counts_day1$Ordering)] <- '27'
counts_day1$raw_count[is.na(counts_day1$raw_count)] <- 0
#transform wide
counts_day1_wide<-counts_day1 %>% pivot_wider(names_from=`Ordering`,values_from = `raw_count`)
counts_day1_wide[is.na(counts_day1_wide)] <- 0
counts_day1_wide<-counts_day1_wide %>% remove_rownames %>% column_to_rownames(var="sample_ID")
#count species per property
sppr <- specnumber(counts_day1_wide)
head(sppr)## Apple_Hawkes Bay_104 Moteo-Pa road _2018_Day_1
## 4
## Apple_Hawkes Bay_185 Lawn road, Hasting_2019_Day_1
## 2
## Apple_Hawkes Bay_2080 Pakawhai Road_2019_Day_1
## 2
## Apple_Hawkes Bay_241 Evenden Road, Twyford_2018_Day_1
## 3
## Apple_Hawkes Bay_241 Evenden Road, Twyford_2019_Day_1
## 1
## Apple_Hawkes Bay_382 Twyford Rd_2018_Day_1
## 1#sppr
#zeros are in there now, just because of the merge above they are out of order
####################################################
#impact of crop, region and flowering time
###################################################
#crop
sppr_aov <-aov(sppr ~ Crop, data = meta_data_day1)
summary(sppr_aov)## Df Sum Sq Mean Sq F value Pr(>F)
## Crop 7 9282 1325.9 100 <2e-16 ***
## Residuals 288 3819 13.3
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(sppr_aov)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = sppr ~ Crop, data = meta_data_day1)
##
## $Crop
## diff lwr upr p adj
## Avocado-Apple 4.56864564 1.7152254 7.4220659 0.0000459
## Carrot-Apple 12.82535885 10.0346744 15.6160433 0.0000000
## Kiwifruit-Apple 1.46503497 -1.3627657 4.2928357 0.7608700
## Onion-Apple 14.65272727 11.8081623 17.4972922 0.0000000
## Pak Choi-Apple 12.65077605 9.7123623 15.5891898 0.0000000
## Pear-Apple 1.41558442 -2.3856254 5.2167942 0.9480974
## Radish-Apple 6.04545455 1.9396787 10.1512304 0.0002672
## Carrot-Avocado 8.25671321 6.0906885 10.4227380 0.0000000
## Kiwifruit-Avocado -3.10361068 -5.3172503 -0.8899710 0.0006623
## Onion-Avocado 10.08408163 7.8490663 12.3190969 0.0000000
## Pak Choi-Avocado 8.08213041 5.7288307 10.4354301 0.0000000
## Pear-Avocado -3.15306122 -6.5224736 0.2163511 0.0854379
## Radish-Avocado 1.47680891 -2.2327920 5.1864098 0.9268761
## Kiwifruit-Carrot -11.36032389 -13.4924854 -9.2281624 0.0000000
## Onion-Carrot 1.82736842 -0.3269773 3.9817142 0.1638473
## Pak Choi-Carrot -0.17458280 -2.4514077 2.1022421 0.9999980
## Pear-Carrot -11.40977444 -14.7262261 -8.0933227 0.0000000
## Radish-Carrot -6.77990431 -10.4414683 -3.1183403 0.0000010
## Onion-Kiwifruit 13.18769231 10.9854791 15.3899055 0.0000000
## Pak Choi-Kiwifruit 11.18574109 8.8635721 13.5079101 0.0000000
## Pear-Kiwifruit -0.04945055 -3.3971944 3.2982933 1.0000000
## Radish-Kiwifruit 4.58041958 0.8904889 8.2703503 0.0044896
## Pak Choi-Onion -2.00195122 -4.3445058 0.3406033 0.1567626
## Pear-Onion -13.23714286 -16.5990593 -9.8752264 0.0000000
## Radish-Onion -8.60727273 -12.3100665 -4.9044790 0.0000000
## Pear-Pak Choi -11.23519164 -14.6768784 -7.7935048 0.0000000
## Radish-Pak Choi -6.60532151 -10.3806900 -2.8299530 0.0000052
## Radish-Pear 4.62987013 0.1501014 9.1096388 0.0371083#impact of region
sppr_aov <-aov(sppr ~ Region, data = meta_data_day1)
summary(sppr_aov)## Df Sum Sq Mean Sq F value Pr(>F)
## Region 11 8256 750.5 44 <2e-16 ***
## Residuals 284 4845 17.1
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(sppr_aov)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = sppr ~ Region, data = meta_data_day1)
##
## $Region
## diff lwr upr p adj
## Bay of Plenty-Auckland -9.0000000 -16.08358054 -1.9164195 0.0021958
## Canterbury-Auckland -0.5673077 -7.50263546 6.3680201 1.0000000
## Hawkes Bay-Auckland -10.3833333 -17.62853726 -3.1381294 0.0002272
## Marlborough-Auckland 2.6309524 -4.79465769 10.0565624 0.9909283
## Northland-Auckland -8.4375000 -16.04648608 -0.8285139 0.0158279
## Otago-Auckland 4.3611111 -3.81830315 12.5405254 0.8394588
## Poverty Bay-Auckland -12.5000000 -20.83522663 -4.1647734 0.0000835
## Southland-Auckland 0.9166667 -7.26274759 9.0960809 0.9999999
## Tasman-Auckland -11.2812500 -18.49976801 -4.0627320 0.0000311
## Wairarapa-Auckland 0.4722222 -7.70719204 8.6516365 1.0000000
## Whangarei-Auckland -7.7500000 -16.53610032 1.0361003 0.1440736
## Canterbury-Bay of Plenty 8.4326923 6.05756833 10.8078163 0.0000000
## Hawkes Bay-Bay of Plenty -1.3833333 -4.55120685 1.7845402 0.9547151
## Marlborough-Bay of Plenty 11.6309524 8.06975490 15.1921499 0.0000000
## Northland-Bay of Plenty 0.5625000 -3.36676351 4.4917635 0.9999986
## Otago-Bay of Plenty 13.3611111 8.41689628 18.3053259 0.0000000
## Poverty Bay-Bay of Plenty -3.5000000 -8.69792705 1.6979270 0.5372219
## Southland-Bay of Plenty 9.9166667 4.97245184 14.8608815 0.0000000
## Tasman-Bay of Plenty -2.2812500 -5.38760556 0.8251056 0.3959120
## Wairarapa-Bay of Plenty 9.4722222 4.52800739 14.4164371 0.0000001
## Whangarei-Bay of Plenty 1.2500000 -4.64389527 7.1438953 0.9999206
## Hawkes Bay-Canterbury -9.8160256 -12.63685543 -6.9951959 0.0000000
## Marlborough-Canterbury 3.1982601 -0.05808536 6.4546055 0.0594537
## Northland-Canterbury -7.8701923 -11.52543099 -4.2149536 0.0000000
## Otago-Canterbury 4.9284188 0.19905116 9.6577864 0.0326544
## Poverty Bay-Canterbury -11.9326923 -16.92669908 -6.9386855 0.0000000
## Southland-Canterbury 1.4839744 -3.24539329 6.2133420 0.9967869
## Tasman-Canterbury -10.7139423 -13.46550602 -7.9623786 0.0000000
## Wairarapa-Canterbury 1.0395299 -3.68983773 5.7688976 0.9998867
## Whangarei-Canterbury -7.1826923 -12.89755534 -1.4678293 0.0026257
## Marlborough-Hawkes Bay 13.0142857 9.14156131 16.8870101 0.0000000
## Northland-Hawkes Bay 1.9458333 -2.26783235 6.1594990 0.9335068
## Otago-Hawkes Bay 14.7444444 9.57132865 19.9175602 0.0000000
## Poverty Bay-Hawkes Bay -2.1166667 -7.53278265 3.2994493 0.9801023
## Southland-Hawkes Bay 11.3000000 6.12688420 16.4731158 0.0000000
## Tasman-Hawkes Bay -0.8979167 -4.35700782 2.5611745 0.9994333
## Wairarapa-Hawkes Bay 10.8555556 5.68243976 16.0286714 0.0000000
## Whangarei-Hawkes Bay 2.6333333 -3.45385553 8.7205222 0.9576264
## Northland-Marlborough -11.0684524 -15.58527465 -6.5516301 0.0000000
## Otago-Marlborough 1.7301587 -3.69274012 7.1530576 0.9962816
## Poverty Bay-Marlborough -15.1309524 -20.78612853 -9.4757762 0.0000000
## Southland-Marlborough -1.7142857 -7.13718456 3.7086131 0.9965699
## Tasman-Marlborough -13.9122024 -17.73476912 -10.0896356 0.0000000
## Wairarapa-Marlborough -2.1587302 -7.58162901 3.2641687 0.9770728
## Whangarei-Marlborough -10.3809524 -16.68179146 -4.0801133 0.0000078
## Otago-Northland 12.7986111 7.12720774 18.4700145 0.0000000
## Poverty Bay-Northland -4.0625000 -9.95639527 1.8313953 0.4993686
## Southland-Northland 9.3541667 3.68276330 15.0255700 0.0000076
## Tasman-Northland -2.8437500 -7.01136331 1.3238633 0.5157890
## Wairarapa-Northland 8.9097222 3.23831885 14.5811256 0.0000273
## Whangarei-Northland 0.6875000 -5.82844639 7.2034464 0.9999999
## Poverty Bay-Otago -16.8611111 -23.47504716 -10.2471751 0.0000000
## Southland-Otago -3.4444444 -9.86090489 2.9720160 0.8333128
## Tasman-Otago -15.6423611 -20.77803529 -10.5066869 0.0000000
## Wairarapa-Otago -3.8888889 -10.30534934 2.5275716 0.6949111
## Whangarei-Otago -12.1111111 -19.28493198 -4.9372902 0.0000040
## Southland-Poverty Bay 13.4166667 6.80273062 20.0306027 0.0000000
## Tasman-Poverty Bay 1.2187500 -4.16161565 6.5991157 0.9998477
## Wairarapa-Poverty Bay 12.9722222 6.35828618 19.5861583 0.0000000
## Whangarei-Poverty Bay 4.7500000 -2.60097893 12.1009789 0.6020319
## Tasman-Southland -12.1979167 -17.33359085 -7.0622425 0.0000000
## Wairarapa-Southland -0.4444444 -6.86090489 5.9720160 1.0000000
## Whangarei-Southland -8.6666667 -15.84048754 -1.4928458 0.0048789
## Wairarapa-Tasman 11.7534722 6.61779804 16.8891464 0.0000000
## Whangarei-Tasman 3.5312500 -2.52415176 9.5866518 0.7446545
## Whangarei-Wairarapa -8.2222222 -15.39604309 -1.0484014 0.0103143#impact of crop and region
sppr_aov <-aov(sppr ~ Crop+Region, data = meta_data_day1)
summary(sppr_aov)## Df Sum Sq Mean Sq F value Pr(>F)
## Crop 7 9282 1325.9 101.363 <2e-16 ***
## Region 11 195 17.8 1.358 0.193
## Residuals 277 3623 13.1
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(sppr_aov)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = sppr ~ Crop + Region, data = meta_data_day1)
##
## $Crop
## diff lwr upr p adj
## Avocado-Apple 4.56864564 1.733688 7.4036033 0.0000401
## Carrot-Apple 12.82535885 10.052731 15.5979866 0.0000000
## Kiwifruit-Apple 1.46503497 -1.344469 4.2745388 0.7544528
## Onion-Apple 14.65272727 11.826568 17.4788869 0.0000000
## Pak Choi-Apple 12.65077605 9.731375 15.5701773 0.0000000
## Pear-Apple 1.41558442 -2.361030 5.1921991 0.9461956
## Radish-Apple 6.04545455 1.966244 10.1246647 0.0002373
## Carrot-Avocado 8.25671321 6.104703 10.4087231 0.0000000
## Kiwifruit-Avocado -3.10361068 -5.302927 -0.9042940 0.0005938
## Onion-Avocado 10.08408163 7.863528 12.3046357 0.0000000
## Pak Choi-Avocado 8.08213041 5.744057 10.4202035 0.0000000
## Pear-Avocado -3.15306122 -6.500672 0.1945499 0.0813428
## Radish-Avocado 1.47680891 -2.208790 5.1624075 0.9243173
## Kiwifruit-Carrot -11.36032389 -13.478690 -9.2419582 0.0000000
## Onion-Carrot 1.82736842 -0.313038 3.9677749 0.1575968
## Pak Choi-Carrot -0.17458280 -2.436676 2.0875103 0.9999979
## Pear-Carrot -11.40977444 -14.704768 -8.1147813 0.0000000
## Radish-Carrot -6.77990431 -10.417777 -3.1420318 0.0000009
## Onion-Kiwifruit 13.18769231 10.999728 15.3756565 0.0000000
## Pak Choi-Kiwifruit 11.18574109 8.878597 13.4928849 0.0000000
## Pear-Kiwifruit -0.04945055 -3.375533 3.2766323 1.0000000
## Radish-Kiwifruit 4.58041958 0.914364 8.2464752 0.0041119
## Pak Choi-Onion -2.00195122 -4.329349 0.3254462 0.1506732
## Pear-Onion -13.23714286 -16.577307 -9.8969791 0.0000000
## Radish-Onion -8.60727273 -12.286108 -4.9284373 0.0000000
## Pear-Pak Choi -11.23519164 -14.654610 -7.8157737 0.0000000
## Radish-Pak Choi -6.60532151 -10.356262 -2.8543809 0.0000044
## Radish-Pear 4.62987013 0.179087 9.0806533 0.0349090
##
## $Region
## diff lwr upr p adj
## Bay of Plenty-Auckland 0.43996008 -5.7644226 6.6443428 1.0000000
## Canterbury-Auckland -0.24638096 -6.3209117 5.8281498 1.0000000
## Hawkes Bay-Auckland 0.37411004 -5.9718357 6.7200558 1.0000000
## Marlborough-Auckland 0.62900116 -5.8749592 7.1329615 1.0000000
## Northland-Auckland -0.35536959 -7.0199457 6.3092065 1.0000000
## Otago-Auckland 3.17132363 -3.9928804 10.3355277 0.9500725
## Poverty Bay-Auckland -1.31425891 -8.6149363 5.9864184 0.9999848
## Southland-Auckland 0.91666667 -6.2475374 8.0808707 0.9999996
## Tasman-Auckland 0.28620313 -6.0363689 6.6087752 1.0000000
## Wairarapa-Auckland -1.52972900 -8.6939331 5.6344751 0.9999147
## Whangarei-Auckland 0.33213041 -7.3634592 8.0277200 1.0000000
## Canterbury-Bay of Plenty -0.68634105 -2.7666701 1.3939880 0.9950136
## Hawkes Bay-Bay of Plenty -0.06585005 -2.8405343 2.7088343 1.0000000
## Marlborough-Bay of Plenty 0.18904108 -2.9301487 3.3082309 1.0000000
## Northland-Bay of Plenty -0.79532967 -4.2369020 2.6462426 0.9998141
## Otago-Bay of Plenty 2.73136355 -1.5991866 7.0619137 0.6381495
## Poverty Bay-Bay of Plenty -1.75421900 -6.3069912 2.7985532 0.9821359
## Southland-Bay of Plenty 0.47670658 -3.8538436 4.8072568 0.9999999
## Tasman-Bay of Plenty -0.15375696 -2.8745588 2.5670449 1.0000000
## Wairarapa-Bay of Plenty -1.96968908 -6.3002393 2.3608611 0.9398406
## Whangarei-Bay of Plenty -0.10782967 -5.2701881 5.0545288 1.0000000
## Hawkes Bay-Canterbury 0.62049100 -1.8502238 3.0912058 0.9995847
## Marlborough-Canterbury 0.87538212 -1.9767932 3.7275574 0.9973496
## Northland-Canterbury -0.10898863 -3.3105474 3.0925702 1.0000000
## Otago-Canterbury 3.41770460 -0.7246647 7.5600739 0.2222689
## Poverty Bay-Canterbury -1.06787795 -5.4420400 3.3062841 0.9996834
## Southland-Canterbury 1.16304763 -2.9793217 5.3054170 0.9988168
## Tasman-Canterbury 0.53258409 -1.8774618 2.9426300 0.9998801
## Wairarapa-Canterbury -1.28334804 -5.4257174 2.8590213 0.9971195
## Whangarei-Canterbury 0.57851137 -4.4270359 5.5840587 0.9999999
## Marlborough-Hawkes Bay 0.25489113 -3.1371596 3.6469418 1.0000000
## Northland-Hawkes Bay -0.72947962 -4.4201548 2.9611955 0.9999606
## Otago-Hawkes Bay 2.79721360 -1.7338269 7.3282541 0.6691088
## Poverty Bay-Hawkes Bay -1.68836895 -6.4322490 3.0555111 0.9905593
## Southland-Hawkes Bay 0.54255663 -3.9884839 5.0735971 0.9999998
## Tasman-Hawkes Bay -0.08790691 -3.1176636 2.9418498 1.0000000
## Wairarapa-Hawkes Bay -1.90383903 -6.4348795 2.6272015 0.9655847
## Whangarei-Hawkes Bay -0.04197962 -5.3736405 5.2896813 1.0000000
## Northland-Marlborough -0.98437075 -4.9405754 2.9718339 0.9996200
## Otago-Marlborough 2.54232247 -2.2074986 7.2921435 0.8357475
## Poverty Bay-Marlborough -1.94326007 -6.8965287 3.0100085 0.9794491
## Southland-Marlborough 0.28766551 -4.4621555 5.0374865 1.0000000
## Tasman-Marlborough -0.34279804 -3.6909165 3.0053205 1.0000000
## Wairarapa-Marlborough -2.15873016 -6.9085512 2.5910909 0.9401530
## Whangarei-Marlborough -0.29687075 -5.8156641 5.2219226 1.0000000
## Otago-Northland 3.52669322 -1.4407885 8.4941750 0.4507462
## Poverty Bay-Northland -0.95888932 -6.1212478 4.2034691 0.9999790
## Southland-Northland 1.27203625 -3.6954455 6.2395180 0.9995002
## Tasman-Northland 0.64157271 -3.0087660 4.2919114 0.9999881
## Wairarapa-Northland -1.17435941 -6.1418412 3.7931223 0.9997680
## Whangarei-Northland 0.68750000 -5.0197020 6.3947020 0.9999998
## Poverty Bay-Otago -4.48558255 -10.2786120 1.3074469 0.3124387
## Southland-Otago -2.25465697 -7.8747210 3.3654071 0.9756174
## Tasman-Otago -2.88512051 -7.3833665 1.6131255 0.6130594
## Wairarapa-Otago -4.70105263 -10.3211167 0.9190114 0.2046399
## Whangarei-Otago -2.83919322 -9.1226158 3.4442294 0.9424718
## Southland-Poverty Bay 2.23092558 -3.5621038 8.0239550 0.9822093
## Tasman-Poverty Bay 1.60046204 -3.1121049 6.3130290 0.9936163
## Wairarapa-Poverty Bay -0.21547009 -6.0084995 5.5775593 1.0000000
## Whangarei-Poverty Bay 1.64638932 -4.7922029 8.0849816 0.9995069
## Tasman-Southland -0.63046354 -5.1287096 3.8677825 0.9999989
## Wairarapa-Southland -2.44639566 -8.0664597 3.1736684 0.9556211
## Whangarei-Southland -0.58453625 -6.8679589 5.6988864 1.0000000
## Wairarapa-Tasman -1.81593212 -6.3141782 2.6823139 0.9744347
## Whangarei-Tasman 0.04592729 -5.2578918 5.3497464 1.0000000
## Whangarei-Wairarapa 1.86185941 -4.4215632 8.1452820 0.9980658#note region is no longer significant when we drop those other samples
#region within a crop
#I did this for beta and then decided I prefer it because of the biased sampling across crops/regions
#radish has only one location
categories_crop <- meta_data_day1 %>% filter(Crop!='Radish') %>% select(Crop) %>% unique()
categories_crop$Crop <- gsub("Pak Choi", "Pakchoi", categories_crop$Crop)
for (item in categories_crop$Crop){
print(paste('Analysing',item))
#filtered_sppr <- sppr[grepl('^Apple', names(sppr))]
filtered_sppr <- sppr[grepl(paste0('^',item), names(sppr))]
filtered_meta_data_day1<-meta_data_day1 %>% filter(sample_ID %in% names(filtered_sppr))
sppr_aov <-aov(filtered_sppr ~ Region, data = filtered_meta_data_day1)
print(summary(sppr_aov))
print(TukeyHSD(sppr_aov))
}## [1] "Analysing Apple"
## Df Sum Sq Mean Sq F value Pr(>F)
## Region 1 0.274 0.2744 0.214 0.648
## Residuals 20 25.589 1.2795
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = filtered_sppr ~ Region, data = filtered_meta_data_day1)
##
## $Region
## diff lwr upr p adj
## Tasman-Hawkes Bay 0.2321429 -0.8135956 1.277881 0.6483192
##
## [1] "Analysing Pear"
## Df Sum Sq Mean Sq F value Pr(>F)
## Region 1 38.06 38.06 11.67 0.00512 **
## Residuals 12 39.15 3.26
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = filtered_sppr ~ Region, data = filtered_meta_data_day1)
##
## $Region
## diff lwr upr p adj
## Tasman-Hawkes Bay -3.65 -5.978248 -1.321752 0.0051188
##
## [1] "Analysing Avocado"
## Df Sum Sq Mean Sq F value Pr(>F)
## Region 2 5.6 2.779 0.258 0.774
## Residuals 46 496.4 10.791
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = filtered_sppr ~ Region, data = filtered_meta_data_day1)
##
## $Region
## diff lwr upr p adj
## Northland-Bay of Plenty -0.72453704 -3.234532 1.785458 0.7652147
## Whangarei-Bay of Plenty -0.03703704 -3.627745 3.553671 0.9996560
## Whangarei-Northland 0.68750000 -3.121021 4.496021 0.9002447
##
## [1] "Analysing Kiwifruit"
## Df Sum Sq Mean Sq F value Pr(>F)
## Region 3 48.69 16.229 4.797 0.0053 **
## Residuals 48 162.39 3.383
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = filtered_sppr ~ Region, data = filtered_meta_data_day1)
##
## $Region
## diff lwr upr p adj
## Hawkes Bay-Bay of Plenty -1.6507937 -3.6010554 0.2994681 0.1238499
## Poverty Bay-Bay of Plenty -1.8452381 -3.8790348 0.1885586 0.0878478
## Tasman-Bay of Plenty 0.6190476 -1.0699286 2.3080238 0.7639800
## Poverty Bay-Hawkes Bay -0.1944444 -2.5730443 2.1841554 0.9963139
## Tasman-Hawkes Bay 2.2698413 0.1784186 4.3612640 0.0286208
## Tasman-Poverty Bay 2.4642857 0.2947565 4.6338149 0.0202320
##
## [1] "Analysing Pakchoi"
## Df Sum Sq Mean Sq F value Pr(>F)
## Region 3 45.1 15.03 0.907 0.447
## Residuals 37 613.3 16.58
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = filtered_sppr ~ Region, data = filtered_meta_data_day1)
##
## $Region
## diff lwr upr p adj
## Canterbury-Auckland -0.5100000 -6.407272 5.387272 0.9954882
## Hawkes Bay-Auckland 3.2500000 -5.113934 11.613934 0.7242716
## Southland-Auckland 0.9166667 -5.664041 7.497375 0.9817868
## Hawkes Bay-Canterbury 3.7600000 -2.931147 10.451147 0.4410908
## Southland-Canterbury 1.4266667 -2.830301 5.683635 0.8041139
## Southland-Hawkes Bay -2.3333333 -9.633973 4.967307 0.8253941
##
## [1] "Analysing Onion"
## Df Sum Sq Mean Sq F value Pr(>F)
## Region 3 34.2 11.41 0.462 0.71
## Residuals 46 1137.0 24.72
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = filtered_sppr ~ Region, data = filtered_meta_data_day1)
##
## $Region
## diff lwr upr p adj
## Marlborough-Canterbury 0.5142857 -3.965768 4.994340 0.9899256
## Otago-Canterbury 0.9333333 -5.910062 7.776729 0.9833602
## Wairarapa-Canterbury -1.6444444 -7.232053 3.943165 0.8611163
## Otago-Marlborough 0.4190476 -6.175413 7.013509 0.9982465
## Wairarapa-Marlborough -2.1587302 -7.438524 3.121064 0.6974441
## Wairarapa-Otago -2.5777778 -9.969490 4.813934 0.7891899
##
## [1] "Analysing Carrot"
## Df Sum Sq Mean Sq F value Pr(>F)
## Region 1 139.6 139.6 8.024 0.00644 **
## Residuals 55 957.2 17.4
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = filtered_sppr ~ Region, data = filtered_meta_data_day1)
##
## $Region
## diff lwr upr p adj
## Otago-Canterbury 6.127358 1.792274 10.46244 0.0064408#impact of flowering time
sppr_aov <-aov(sppr ~ flowertime, data = meta_data_day1)
summary(sppr_aov)## Df Sum Sq Mean Sq F value Pr(>F)
## flowertime 1 8193 8193 490.8 <2e-16 ***
## Residuals 294 4908 17
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#TukeyHSD(sppr_aov)
#can plot that out
sppr_df <- sppr %>%
enframe() %>%
full_join(meta_data_day1, by = c("name" = "sample_ID"))
#sppr_df$Crop <- factor(sppr_df$Crop, levels = c("Apple","Pear","Avocado","Kiwifruit","Pak Choi","Carrot", "Onion","Radish"))
sppr_df$Crop <- factor(sppr_df$Crop, levels = c("Avocado","Apple","Pear","Kiwifruit","Pak Choi","Radish", "Carrot","Onion"))
#just crop
plot_sppr <- ggplot(sppr_df, aes(x = Crop, y = value, fill = Crop)) +
geom_boxplot() +
scale_fill_manual(values=col_bound)+
theme_bw()+
labs(x = "Crop",
y = "Number of species per sample",
title = "Species richness")
plot_sppr
#other significant factor was flowertime
plot_sppr <- ggplot(sppr_df, aes(x = Crop, y = value, fill = Crop)) +
geom_boxplot() +
facet_wrap(~flowertime, scales = "free_x")+
theme_bw()+
scale_fill_manual(values=col_bound)+
labs(x = "Region",
y = "Number of species per sample",
title = "Species richness")+
theme(axis.text.x = element_text(angle = 90,size=7,hjust=1))
plot_sppr
Simpson day 1
#using simpson which is less sensitive to rare species which should be more robust to the sampling biases in the data
simpsondiv<-diversity(counts_day1_wide,index='simpson')
head(simpsondiv)## Apple_Hawkes Bay_104 Moteo-Pa road _2018_Day_1
## 0.43555556
## Apple_Hawkes Bay_185 Lawn road, Hasting_2019_Day_1
## 0.50000000
## Apple_Hawkes Bay_2080 Pakawhai Road_2019_Day_1
## 0.04542996
## Apple_Hawkes Bay_241 Evenden Road, Twyford_2018_Day_1
## 0.44897959
## Apple_Hawkes Bay_241 Evenden Road, Twyford_2019_Day_1
## 0.00000000
## Apple_Hawkes Bay_382 Twyford Rd_2018_Day_1
## 0.00000000#simpsondiv
####################################################
#impact of crop, region and flowering time
###################################################
#crop
simpsondiv_aov <-aov(simpsondiv ~ Crop, data = meta_data_day1)
summary(simpsondiv_aov)## Df Sum Sq Mean Sq F value Pr(>F)
## Crop 7 6.678 0.954 16.43 <2e-16 ***
## Residuals 288 16.717 0.058
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(simpsondiv_aov)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = simpsondiv ~ Crop, data = meta_data_day1)
##
## $Crop
## diff lwr upr p adj
## Avocado-Apple 0.10568504 -0.0831058977 0.2944759767 0.6812580
## Carrot-Apple 0.39081652 0.2061763819 0.5754566662 0.0000000
## Kiwifruit-Apple 0.02257369 -0.1645221801 0.2096695558 0.9999558
## Onion-Apple 0.37862659 0.1904215503 0.5668316343 0.0000001
## Pak Choi-Apple 0.19410328 -0.0003110861 0.3885176468 0.0507012
## Pear-Apple 0.22402144 -0.0274781303 0.4755210024 0.1208516
## Radish-Apple 0.13408860 -0.1375619821 0.4057391763 0.8032062
## Carrot-Avocado 0.28513148 0.1418207199 0.4284422492 0.0000001
## Kiwifruit-Avocado -0.08311135 -0.2295724624 0.0633497591 0.6658494
## Onion-Avocado 0.27294155 0.1250661626 0.4208169429 0.0000012
## Pak Choi-Avocado 0.08841824 -0.0672832012 0.2441196828 0.6650263
## Pear-Avocado 0.11833640 -0.1045941421 0.3412669351 0.7374438
## Radish-Avocado 0.02840356 -0.2170348876 0.2738420027 0.9999668
## Kiwifruit-Carrot -0.36824284 -0.5093131062 -0.2271725661 0.0000000
## Onion-Carrot -0.01218993 -0.1547279798 0.1303481163 0.9999958
## Pak Choi-Carrot -0.19671324 -0.3473548820 -0.0460716055 0.0021407
## Pear-Carrot -0.16679509 -0.3862215886 0.0526314126 0.2858009
## Radish-Carrot -0.25672793 -0.4989881063 -0.0144677477 0.0291358
## Onion-Kiwifruit 0.35605290 0.2103478021 0.5017580068 0.0000000
## Pak Choi-Kiwifruit 0.17152959 0.0178878505 0.3251713345 0.0168393
## Pear-Kiwifruit 0.20144775 -0.0200491387 0.4229446350 0.1049291
## Radish-Kiwifruit 0.11151491 -0.1326220937 0.3556519121 0.8592171
## Pak Choi-Onion -0.18452331 -0.3395138244 -0.0295327996 0.0078271
## Pear-Onion -0.15460516 -0.3770397420 0.0678294295 0.4030935
## Radish-Onion -0.24453800 -0.4895260566 0.0004500662 0.0508057
## Pear-Pak Choi 0.02991816 -0.1977942791 0.2576305905 0.9999213
## Radish-Pak Choi -0.06001468 -0.3098045114 0.1897751449 0.9959104
## Radish-Pear -0.08993284 -0.3863279203 0.2064622424 0.9833045#impact of region
simpsondiv_aov <-aov(simpsondiv ~ Region, data = meta_data_day1)
summary(simpsondiv_aov)## Df Sum Sq Mean Sq F value Pr(>F)
## Region 11 5.551 0.5046 8.031 3.42e-12 ***
## Residuals 284 17.844 0.0628
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(simpsondiv_aov)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = simpsondiv ~ Region, data = meta_data_day1)
##
## $Region
## diff lwr upr p adj
## Bay of Plenty-Auckland 0.056939646 -0.372967210 0.48684650 0.9999994
## Canterbury-Auckland 0.336081164 -0.084828140 0.75699047 0.2673302
## Hawkes Bay-Auckland 0.081731377 -0.357984502 0.52144726 0.9999790
## Marlborough-Auckland 0.365304571 -0.085360267 0.81596941 0.2461872
## Northland-Auckland 0.170887793 -0.290906247 0.63268183 0.9870681
## Otago-Auckland 0.436486777 -0.059926900 0.93290045 0.1473536
## Poverty Bay-Auckland -0.024253364 -0.530123389 0.48161666 1.0000000
## Southland-Auckland 0.139927882 -0.356485796 0.63634156 0.9987771
## Tasman-Auckland 0.108342157 -0.329754136 0.54643845 0.9996429
## Wairarapa-Auckland 0.363981971 -0.132431706 0.86039565 0.3984638
## Whangarei-Auckland 0.091202301 -0.442031526 0.62443613 0.9999910
## Canterbury-Bay of Plenty 0.279141518 0.134993784 0.42328925 0.0000000
## Hawkes Bay-Bay of Plenty 0.024791731 -0.167468460 0.21705192 0.9999995
## Marlborough-Bay of Plenty 0.308364925 0.092233661 0.52449619 0.0002496
## Northland-Bay of Plenty 0.113948147 -0.124521270 0.35241756 0.9168400
## Otago-Bay of Plenty 0.379547131 0.079479695 0.67961457 0.0023651
## Poverty Bay-Bay of Plenty -0.081193009 -0.396658396 0.23427238 0.9994773
## Southland-Bay of Plenty 0.082988236 -0.217079200 0.38305567 0.9989741
## Tasman-Bay of Plenty 0.051402511 -0.137124116 0.23992914 0.9991013
## Wairarapa-Bay of Plenty 0.307042326 0.006974889 0.60710976 0.0396349
## Whangarei-Bay of Plenty 0.034262655 -0.323441470 0.39196678 1.0000000
## Hawkes Bay-Canterbury -0.254349787 -0.425547680 -0.08315189 0.0001033
## Marlborough-Canterbury 0.029223407 -0.168406198 0.22685301 0.9999980
## Northland-Canterbury -0.165193371 -0.387032052 0.05664531 0.3735864
## Otago-Canterbury 0.100405614 -0.186622615 0.38743384 0.9918404
## Poverty Bay-Canterbury -0.360334527 -0.663423867 -0.05724519 0.0061840
## Southland-Canterbury -0.196153282 -0.483181510 0.09087495 0.5132980
## Tasman-Canterbury -0.227739007 -0.394733099 -0.06074491 0.0006177
## Wairarapa-Canterbury 0.027900808 -0.259127421 0.31492904 1.0000000
## Whangarei-Canterbury -0.244878863 -0.591717412 0.10195969 0.4600955
## Marlborough-Hawkes Bay 0.283573194 0.048535171 0.51861122 0.0049768
## Northland-Hawkes Bay 0.089156416 -0.166573543 0.34488638 0.9920662
## Otago-Hawkes Bay 0.354755401 0.040795824 0.66871498 0.0124516
## Poverty Bay-Hawkes Bay -0.105984740 -0.434692147 0.22272267 0.9959236
## Southland-Hawkes Bay 0.058196505 -0.255763071 0.37215608 0.9999796
## Tasman-Hawkes Bay 0.026610780 -0.183323587 0.23654515 0.9999996
## Wairarapa-Hawkes Bay 0.282250595 -0.031708982 0.59621017 0.1253785
## Whangarei-Hawkes Bay 0.009470924 -0.359964308 0.37890616 1.0000000
## Northland-Marlborough -0.194416778 -0.468545497 0.07971194 0.4527233
## Otago-Marlborough 0.071182206 -0.257936857 0.40030127 0.9999035
## Poverty Bay-Marlborough -0.389557934 -0.732774049 -0.04634182 0.0117414
## Southland-Marlborough -0.225376689 -0.554495752 0.10374237 0.5099896
## Tasman-Marlborough -0.256962414 -0.488956338 -0.02496849 0.0160541
## Wairarapa-Marlborough -0.001322599 -0.330441663 0.32779646 1.0000000
## Whangarei-Marlborough -0.274102270 -0.656504065 0.10829953 0.4353429
## Otago-Northland 0.265598984 -0.078601971 0.60979994 0.3178899
## Poverty Bay-Northland -0.195141157 -0.552845282 0.16256297 0.8180431
## Southland-Northland -0.030959911 -0.375160867 0.31324104 1.0000000
## Tasman-Northland -0.062545636 -0.315480649 0.19038938 0.9996432
## Wairarapa-Northland 0.193094178 -0.151106777 0.53729513 0.7893028
## Whangarei-Northland -0.079685492 -0.475142282 0.31577130 0.9999524
## Poverty Bay-Otago -0.460740141 -0.862143983 -0.05933630 0.0101131
## Southland-Otago -0.296558895 -0.685977823 0.09286003 0.3381988
## Tasman-Otago -0.328144620 -0.639831842 -0.01645740 0.0291871
## Wairarapa-Otago -0.072504806 -0.461923733 0.31691412 0.9999786
## Whangarei-Otago -0.345284476 -0.780668073 0.09009912 0.2771072
## Southland-Poverty Bay 0.164181245 -0.237222597 0.56558509 0.9718590
## Tasman-Poverty Bay 0.132595521 -0.193942177 0.45913322 0.9733469
## Wairarapa-Poverty Bay 0.388235335 -0.013168508 0.78963918 0.0685135
## Whangarei-Poverty Bay 0.115455665 -0.330679763 0.56159109 0.9994495
## Tasman-Southland -0.031585725 -0.343272946 0.28010150 1.0000000
## Wairarapa-Southland 0.224054090 -0.165364838 0.61347302 0.7607984
## Whangarei-Southland -0.048725581 -0.484109177 0.38665802 0.9999999
## Wairarapa-Tasman 0.255639814 -0.056047407 0.56732704 0.2302464
## Whangarei-Tasman -0.017139856 -0.384645909 0.35036620 1.0000000
## Whangarei-Wairarapa -0.272779670 -0.708163267 0.16260393 0.6482293#impact of crop and region
simpsondiv_aov <-aov(simpsondiv ~ Crop+Region, data = meta_data_day1)
summary(simpsondiv_aov)## Df Sum Sq Mean Sq F value Pr(>F)
## Crop 7 6.678 0.9540 16.261 <2e-16 ***
## Region 11 0.466 0.0424 0.722 0.717
## Residuals 277 16.251 0.0587
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(simpsondiv_aov)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = simpsondiv ~ Crop + Region, data = meta_data_day1)
##
## $Crop
## diff lwr upr p adj
## Avocado-Apple 0.10568504 -0.084171770 0.295541849 0.6871918
## Carrot-Apple 0.39081652 0.205133944 0.576499104 0.0000000
## Kiwifruit-Apple 0.02257369 -0.165578483 0.210725858 0.9999574
## Onion-Apple 0.37862659 0.189358986 0.567894199 0.0000001
## Pak Choi-Apple 0.19410328 -0.001408707 0.389615268 0.0532212
## Pear-Apple 0.22402144 -0.028898042 0.476940914 0.1253339
## Radish-Apple 0.13408860 -0.139095662 0.407272856 0.8074919
## Carrot-Avocado 0.28513148 0.141011618 0.429251351 0.0000001
## Kiwifruit-Avocado -0.08311135 -0.230399350 0.064176647 0.6719470
## Onion-Avocado 0.27294155 0.124231290 0.421651815 0.0000014
## Pak Choi-Avocado 0.08841824 -0.068162258 0.244998739 0.6711324
## Pear-Avocado 0.11833640 -0.105852759 0.342525552 0.7426969
## Radish-Avocado 0.02840356 -0.218420580 0.275227695 0.9999679
## Kiwifruit-Carrot -0.36824284 -0.510109558 -0.226376114 0.0000000
## Onion-Carrot -0.01218993 -0.155532719 0.131152855 0.9999960
## Pak Choi-Carrot -0.19671324 -0.348205372 -0.045221116 0.0023418
## Pear-Carrot -0.16679509 -0.387460423 0.053870247 0.2926213
## Radish-Carrot -0.25672793 -0.500355855 -0.013099999 0.0308126
## Onion-Kiwifruit 0.35605290 0.209525183 0.502580626 0.0000000
## Pak Choi-Kiwifruit 0.17152959 0.017020423 0.326038762 0.0179379
## Pear-Kiwifruit 0.20144775 -0.021299662 0.424195158 0.1090399
## Radish-Kiwifruit 0.11151491 -0.134000438 0.357030257 0.8625246
## Pak Choi-Onion -0.18452331 -0.340388867 -0.028657757 0.0084212
## Pear-Onion -0.15460516 -0.378295559 0.069085247 0.4104300
## Radish-Onion -0.24453800 -0.490909206 0.001833215 0.0533294
## Pear-Pak Choi 0.02991816 -0.199079894 0.258916205 0.9999240
## Radish-Pak Choi -0.06001468 -0.311214770 0.191185404 0.9960407
## Radish-Pear -0.08993284 -0.388001302 0.208135625 0.9838026
##
## $Region
## diff lwr upr p adj
## Bay of Plenty-Auckland 0.1817191030 -0.2337877 0.5972259 0.9541267
## Canterbury-Auckland 0.2155668359 -0.1912438 0.6223775 0.8446837
## Hawkes Bay-Auckland 0.2197826978 -0.2052046 0.6447700 0.8648359
## Marlborough-Auckland 0.1807812588 -0.2547883 0.6163508 0.9685545
## Northland-Auckland 0.2593060338 -0.1870199 0.7056320 0.7489212
## Otago-Auckland 0.2465457178 -0.2332403 0.7263317 0.8697686
## Poverty Bay-Auckland 0.1472762289 -0.3416493 0.6362018 0.9977576
## Southland-Auckland 0.1399278817 -0.3398581 0.6197139 0.9983247
## Tasman-Auckland 0.2225627502 -0.2008592 0.6459847 0.8516085
## Wairarapa-Auckland 0.1794586595 -0.3003273 0.6592446 0.9858712
## Whangarei-Auckland 0.1796205418 -0.3357523 0.6949933 0.9920646
## Canterbury-Bay of Plenty 0.0338477329 -0.1054717 0.1731671 0.9996979
## Hawkes Bay-Bay of Plenty 0.0380635947 -0.1477567 0.2238839 0.9999436
## Marlborough-Bay of Plenty -0.0009378442 -0.2098296 0.2079540 1.0000000
## Northland-Bay of Plenty 0.0775869308 -0.1528948 0.3080687 0.9940771
## Otago-Bay of Plenty 0.0648266147 -0.2251899 0.3548431 0.9998658
## Poverty Bay-Bay of Plenty -0.0344428741 -0.3393415 0.2704558 0.9999999
## Southland-Bay of Plenty -0.0417912213 -0.3318077 0.2482253 0.9999985
## Tasman-Bay of Plenty 0.0408436471 -0.1413682 0.2230554 0.9998621
## Wairarapa-Bay of Plenty -0.0022604435 -0.2922769 0.2877560 1.0000000
## Whangarei-Bay of Plenty -0.0020985612 -0.3478211 0.3436240 1.0000000
## Hawkes Bay-Canterbury 0.0042158618 -0.1612476 0.1696794 1.0000000
## Marlborough-Canterbury -0.0347855771 -0.2257954 0.1562243 0.9999829
## Northland-Canterbury 0.0437391979 -0.1706688 0.2581472 0.9999459
## Otago-Canterbury 0.0309788818 -0.2464351 0.3083929 0.9999999
## Poverty Bay-Canterbury -0.0682906070 -0.3612278 0.2246466 0.9997975
## Southland-Canterbury -0.0756389543 -0.3530530 0.2017751 0.9990981
## Tasman-Canterbury 0.0069959142 -0.1544046 0.1683964 1.0000000
## Wairarapa-Canterbury -0.0361081764 -0.3135222 0.2413058 0.9999995
## Whangarei-Canterbury -0.0359462941 -0.3711673 0.2992747 0.9999999
## Marlborough-Hawkes Bay -0.0390014390 -0.2661667 0.1881638 0.9999906
## Northland-Hawkes Bay 0.0395233361 -0.2076408 0.2866874 0.9999955
## Otago-Hawkes Bay 0.0267630200 -0.2766803 0.3302063 1.0000000
## Poverty Bay-Hawkes Bay -0.0725064688 -0.3902036 0.2451907 0.9998354
## Southland-Hawkes Bay -0.0798548161 -0.3832981 0.2235885 0.9993528
## Tasman-Hawkes Bay 0.0027800524 -0.2001224 0.2056825 1.0000000
## Wairarapa-Hawkes Bay -0.0403240383 -0.3437673 0.2631193 0.9999994
## Whangarei-Hawkes Bay -0.0401621559 -0.3972229 0.3168986 0.9999999
## Northland-Marlborough 0.0785247750 -0.1864218 0.3434714 0.9980619
## Otago-Marlborough 0.0657644590 -0.2523305 0.3838595 0.9999382
## Poverty Bay-Marlborough -0.0335050298 -0.3652249 0.2982148 1.0000000
## Southland-Marlborough -0.0408533771 -0.3589484 0.2772416 0.9999996
## Tasman-Marlborough 0.0417814914 -0.1824416 0.2660046 0.9999783
## Wairarapa-Marlborough -0.0013225993 -0.3194176 0.3167724 1.0000000
## Whangarei-Marlborough -0.0011607170 -0.3707537 0.3684323 1.0000000
## Otago-Northland -0.0127603161 -0.3454320 0.3199114 1.0000000
## Poverty Bay-Northland -0.1120298049 -0.4577524 0.2336928 0.9957298
## Southland-Northland -0.1193781521 -0.4520499 0.2132936 0.9898984
## Tasman-Northland -0.0367432837 -0.2812061 0.2077195 0.9999977
## Wairarapa-Northland -0.0798473743 -0.4125191 0.2528243 0.9997313
## Whangarei-Northland -0.0796854920 -0.4618962 0.3025252 0.9999328
## Poverty Bay-Otago -0.0992694888 -0.4872280 0.2886891 0.9995038
## Southland-Otago -0.1066178361 -0.4829929 0.2697572 0.9987160
## Tasman-Otago -0.0239829676 -0.3252300 0.2772641 1.0000000
## Wairarapa-Otago -0.0670870583 -0.4434621 0.3092880 0.9999862
## Whangarei-Otago -0.0669251759 -0.4877253 0.3538750 0.9999958
## Southland-Poverty Bay -0.0073483473 -0.3953069 0.3806102 1.0000000
## Tasman-Poverty Bay 0.0752865212 -0.2403136 0.3908866 0.9997468
## Wairarapa-Poverty Bay 0.0321824305 -0.3557761 0.4201410 1.0000000
## Whangarei-Poverty Bay 0.0323443129 -0.3988475 0.4635361 1.0000000
## Tasman-Southland 0.0826348685 -0.2186122 0.3838819 0.9990460
## Wairarapa-Southland 0.0395307778 -0.3368443 0.4159059 0.9999999
## Whangarei-Southland 0.0396926601 -0.3811075 0.4604928 1.0000000
## Wairarapa-Tasman -0.0431040907 -0.3443511 0.2581430 0.9999986
## Whangarei-Tasman -0.0429422084 -0.3981384 0.3122540 0.9999998
## Whangarei-Wairarapa 0.0001618823 -0.4206382 0.4209620 1.0000000#region within a crop
#I did this for beta and then decided I prefer it because of the biased sampling across crops/regions
#radish has only one location
categories_crop <- meta_data_day1 %>% filter(Crop!='Radish') %>% select(Crop) %>% unique()
categories_crop$Crop <- gsub("Pak Choi", "Pakchoi", categories_crop$Crop)
for (item in categories_crop$Crop){
print(paste('Analysing',item))
#filtered_sppr <- sppr[grepl('^Apple', names(sppr))]
filtered_simpsondiv <- simpsondiv[grepl(paste0('^',item), names(simpsondiv))]
filtered_meta_data_day1<-meta_data_day1 %>% filter(sample_ID %in% names(filtered_simpsondiv))
simpsondiv_aov <-aov(filtered_simpsondiv ~ Region, data = filtered_meta_data_day1)
print(summary(simpsondiv_aov))
print(TukeyHSD(simpsondiv_aov))
}## [1] "Analysing Apple"
## Df Sum Sq Mean Sq F value Pr(>F)
## Region 1 0.085 0.08509 0.456 0.507
## Residuals 20 3.729 0.18643
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = filtered_simpsondiv ~ Region, data = filtered_meta_data_day1)
##
## $Region
## diff lwr upr p adj
## Tasman-Hawkes Bay -0.1292832 -0.5284604 0.269894 0.5070381
##
## [1] "Analysing Pear"
## Df Sum Sq Mean Sq F value Pr(>F)
## Region 1 0.0166 0.01664 0.138 0.716
## Residuals 12 1.4441 0.12034
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = filtered_simpsondiv ~ Region, data = filtered_meta_data_day1)
##
## $Region
## diff lwr upr p adj
## Tasman-Hawkes Bay 0.07632419 -0.3708278 0.5234762 0.7164509
##
## [1] "Analysing Avocado"
## Df Sum Sq Mean Sq F value Pr(>F)
## Region 2 0.0748 0.03742 0.797 0.457
## Residuals 46 2.1590 0.04694
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = filtered_simpsondiv ~ Region, data = filtered_meta_data_day1)
##
## $Region
## diff lwr upr p adj
## Northland-Bay of Plenty 0.084087300 -0.08144638 0.2496210 0.4417108
## Whangarei-Bay of Plenty 0.004401808 -0.23240462 0.2412082 0.9988833
## Whangarei-Northland -0.079685492 -0.33085664 0.1714857 0.7241302
##
## [1] "Analysing Kiwifruit"
## Df Sum Sq Mean Sq F value Pr(>F)
## Region 3 0.121 0.04044 0.335 0.8
## Residuals 48 5.791 0.12065
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = filtered_simpsondiv ~ Region, data = filtered_meta_data_day1)
##
## $Region
## diff lwr upr p adj
## Hawkes Bay-Bay of Plenty -0.02417588 -0.3924753 0.3441236 0.9980787
## Poverty Bay-Bay of Plenty -0.04280049 -0.4268752 0.3412742 0.9908111
## Tasman-Bay of Plenty 0.08879767 -0.2301590 0.4077544 0.8800454
## Poverty Bay-Hawkes Bay -0.01862461 -0.4678141 0.4305649 0.9995123
## Tasman-Hawkes Bay 0.11297355 -0.2819836 0.5079307 0.8713803
## Tasman-Poverty Bay 0.13159816 -0.2781091 0.5413054 0.8278400
##
## [1] "Analysing Pakchoi"
## Df Sum Sq Mean Sq F value Pr(>F)
## Region 3 0.2918 0.09728 2.726 0.0579 .
## Residuals 37 1.3204 0.03569
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = filtered_simpsondiv ~ Region, data = filtered_meta_data_day1)
##
## $Region
## diff lwr upr p adj
## Canterbury-Auckland 0.24280198 -0.03083279 0.51643674 0.0973993
## Hawkes Bay-Auckland 0.33413560 -0.05395281 0.72222400 0.1127644
## Southland-Auckland 0.13992788 -0.16541844 0.44527421 0.6105373
## Hawkes Bay-Canterbury 0.09133362 -0.21913710 0.40180435 0.8579440
## Southland-Canterbury -0.10287409 -0.30039837 0.09465018 0.5068087
## Southland-Hawkes Bay -0.19420772 -0.53295905 0.14454362 0.4235200
##
## [1] "Analysing Onion"
## Df Sum Sq Mean Sq F value Pr(>F)
## Region 3 0.0411 0.01370 0.847 0.475
## Residuals 46 0.7443 0.01618
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = filtered_simpsondiv ~ Region, data = filtered_meta_data_day1)
##
## $Region
## diff lwr upr p adj
## Marlborough-Canterbury -0.050901985 -0.16552540 0.06372143 0.6400772
## Otago-Canterbury 0.023108232 -0.15198192 0.19819838 0.9848712
## Wairarapa-Canterbury -0.052224584 -0.19518509 0.09073593 0.7649682
## Otago-Marlborough 0.074010217 -0.09471087 0.24273131 0.6490195
## Wairarapa-Marlborough -0.001322599 -0.13640758 0.13376239 0.9999935
## Wairarapa-Otago -0.075332816 -0.26445179 0.11378616 0.7141963
##
## [1] "Analysing Carrot"
## Df Sum Sq Mean Sq F value Pr(>F)
## Region 1 0.0047 0.004691 0.506 0.48
## Residuals 55 0.5098 0.009269
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = filtered_simpsondiv ~ Region, data = filtered_meta_data_day1)
##
## $Region
## diff lwr upr p adj
## Otago-Canterbury 0.03551607 -0.06452693 0.1355591 0.4798109#impact of flowering time
simpsondiv_aov <-aov(simpsondiv ~ flowertime, data = meta_data_day1)
summary(simpsondiv_aov)## Df Sum Sq Mean Sq F value Pr(>F)
## flowertime 1 4.571 4.571 71.4 1.37e-15 ***
## Residuals 294 18.824 0.064
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#TukeyHSD(simpsondiv_aov)
#can plot that out
simpsondiv_df <- simpsondiv %>%
enframe() %>%
full_join(meta_data_day1, by = c("name" = "sample_ID"))
#simpsondiv_df$Crop <- factor(simpsondiv_df$Crop, levels = c("Apple","Pear","Avocado","Kiwifruit","Pak Choi","Carrot", "Onion","Radish"))
simpsondiv_df$Crop <- factor(simpsondiv_df$Crop, levels = c("Avocado","Apple","Pear","Kiwifruit","Pak Choi","Radish", "Carrot","Onion"))
#just crop
plot_sppr <- ggplot(simpsondiv_df, aes(x = Crop, y = value, fill = Crop)) +
geom_boxplot() +
scale_fill_manual(values=col_bound)+
theme_bw()+
labs(x = "Crop",
y = "Simpson",
title = "Species richness: simpson")
plot_sppr
#other significant factor was flowertime
plot_sppr <- ggplot(simpsondiv_df, aes(x = Crop, y = value, fill = Crop)) +
geom_boxplot() +
facet_wrap(~flowertime, scales = "free_x")+
theme_bw()+
scale_fill_manual(values=col_bound)+
labs(x = "Region",
y = "Simpson",
title = "Species richness: simpson")+
theme(axis.text.x = element_text(angle = 90,size=7,hjust=1))
plot_sppr
Shannon day 1
#########
#shannon#
#########
#hmmmm I actually dont like that, when say apple has a bunch of 1's its getting a very high score as its perfectly even
#having a look at shannon
shannondiv<-diversity(counts_day1_wide)
#head(shannondiv)
#crop
shannondiv_aov <-aov(shannondiv ~ Crop, data = meta_data_day1)
summary(shannondiv_aov)## Df Sum Sq Mean Sq F value Pr(>F)
## Crop 7 115.38 16.483 71.47 <2e-16 ***
## Residuals 288 66.42 0.231
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(shannondiv_aov)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = shannondiv ~ Crop, data = meta_data_day1)
##
## $Crop
## diff lwr upr p adj
## Avocado-Apple 0.86043039 0.48410372 1.23675707 0.0000000
## Carrot-Apple 1.79680237 1.42874969 2.16485506 0.0000000
## Kiwifruit-Apple 0.40254673 0.02959892 0.77549454 0.0241881
## Onion-Apple 1.82081378 1.44565499 2.19597256 0.0000000
## Pak Choi-Apple 1.21163937 0.82410322 1.59917552 0.0000000
## Pear-Apple 0.43453680 -0.06679021 0.93586382 0.1438746
## Radish-Apple 0.92837784 0.38688278 1.46987290 0.0000088
## Carrot-Avocado 0.93637198 0.65070327 1.22204069 0.0000000
## Kiwifruit-Avocado -0.45788366 -0.74983212 -0.16593520 0.0000727
## Onion-Avocado 0.96038338 0.66561577 1.25515100 0.0000000
## Pak Choi-Avocado 0.35120898 0.04084129 0.66157666 0.0144610
## Pear-Avocado -0.42589359 -0.87027249 0.01848531 0.0711344
## Radish-Avocado 0.06794745 -0.42129762 0.55719251 0.9998855
## Kiwifruit-Carrot -1.39425564 -1.67545826 -1.11305302 0.0000000
## Onion-Carrot 0.02401140 -0.26011701 0.30813982 0.9999962
## Pak Choi-Carrot -0.58516300 -0.88544472 -0.28488128 0.0000002
## Pear-Carrot -1.36226557 -1.79965969 -0.92487145 0.0000000
## Radish-Carrot -0.86842453 -1.35133420 -0.38551487 0.0000024
## Onion-Kiwifruit 1.41826705 1.12782558 1.70870852 0.0000000
## Pak Choi-Kiwifruit 0.80909264 0.50283066 1.11535462 0.0000000
## Pear-Kiwifruit 0.03199007 -0.40953106 0.47351120 0.9999987
## Radish-Kiwifruit 0.52583111 0.03918027 1.01248195 0.0239241
## Pak Choi-Onion -0.60917441 -0.91812496 -0.30022385 0.0000001
## Pear-Onion -1.38627697 -1.82966727 -0.94288668 0.0000000
## Radish-Onion -0.89243594 -1.38078323 -0.40408864 0.0000015
## Pear-Pak Choi -0.77710257 -1.23101347 -0.32319167 0.0000091
## Radish-Pak Choi -0.28326153 -0.78118043 0.21465737 0.6629593
## Radish-Pear 0.49384104 -0.09697851 1.08466059 0.1782465tukey_test<-TukeyHSD(shannondiv_aov)
tukey_test<-tukey_test$Crop
write.csv(tukey_test,'Alpha_shannon_diversity_onerep_countsvscrops.csv',quote = F,row.names = T)
#impact of region
shannondiv_aov <-aov(shannondiv ~ Region, data = meta_data_day1)
summary(shannondiv_aov)## Df Sum Sq Mean Sq F value Pr(>F)
## Region 11 101.04 9.186 32.3 <2e-16 ***
## Residuals 284 80.77 0.284
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(shannondiv_aov)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = shannondiv ~ Region, data = meta_data_day1)
##
## $Region
## diff lwr upr p adj
## Bay of Plenty-Auckland -0.05618322 -0.970801247 0.858434805 1.0000000
## Canterbury-Auckland 0.86109641 -0.034379508 1.756572330 0.0721780
## Hawkes Bay-Auckland -0.43842117 -1.373907694 0.497065349 0.9266825
## Marlborough-Auckland 1.05937226 0.100592050 2.018152466 0.0165502
## Northland-Auckland 0.20463263 -0.777824726 1.187089992 0.9999336
## Otago-Auckland 1.30348739 0.247377467 2.359597321 0.0035112
## Poverty Bay-Auckland -0.73413021 -1.810358323 0.342097906 0.5162938
## Southland-Auckland 0.22538493 -0.830724998 1.281494857 0.9999155
## Tasman-Auckland -0.32818015 -1.260221041 0.603860734 0.9913840
## Wairarapa-Auckland 0.93032521 -0.125784717 1.986435137 0.1454482
## Whangarei-Auckland 0.07591753 -1.058526509 1.210361573 1.0000000
## Canterbury-Bay of Plenty 0.91727963 0.610608282 1.223950981 0.0000000
## Hawkes Bay-Bay of Plenty -0.38223795 -0.791267568 0.026791664 0.0923402
## Marlborough-Bay of Plenty 1.11555548 0.655740644 1.575370313 0.0000000
## Northland-Bay of Plenty 0.26081585 -0.246522945 0.768154652 0.8696461
## Otago-Bay of Plenty 1.35967061 0.721283293 1.998057936 0.0000000
## Poverty Bay-Bay of Plenty -0.67794699 -1.349093134 -0.006800842 0.0452256
## Southland-Bay of Plenty 0.28156815 -0.356819172 0.919955472 0.9514210
## Tasman-Bay of Plenty -0.27199693 -0.673083470 0.129089604 0.5257678
## Wairarapa-Bay of Plenty 0.98650843 0.348121108 1.624895752 0.0000410
## Whangarei-Bay of Plenty 0.13210075 -0.628907445 0.893108951 0.9999896
## Hawkes Bay-Canterbury -1.29951758 -1.663737593 -0.935297574 0.0000000
## Marlborough-Canterbury 0.19827585 -0.222177088 0.618728782 0.9236394
## Northland-Canterbury -0.65646378 -1.128421027 -0.184506529 0.0004197
## Otago-Canterbury 0.44239098 -0.168255692 1.053037658 0.4179729
## Poverty Bay-Canterbury -1.59522662 -2.240042979 -0.950410260 0.0000000
## Southland-Canterbury -0.63571148 -1.246358156 -0.025064807 0.0330102
## Tasman-Canterbury -1.18927656 -1.544553075 -0.834000054 0.0000000
## Wairarapa-Canterbury 0.06922880 -0.541417876 0.679875473 0.9999999
## Whangarei-Canterbury -0.78517888 -1.523070785 -0.047286972 0.0258903
## Marlborough-Hawkes Bay 1.49779343 0.997754851 1.997832009 0.0000000
## Northland-Hawkes Bay 0.64305381 0.098993556 1.187114055 0.0067278
## Otago-Hawkes Bay 1.74190857 1.073966001 2.409851132 0.0000000
## Poverty Bay-Hawkes Bay -0.29570904 -0.995027310 0.403609237 0.9640362
## Southland-Hawkes Bay 0.66380610 -0.004136464 1.331748668 0.0531253
## Tasman-Hawkes Bay 0.11024102 -0.336390047 0.556872084 0.9996494
## Wairarapa-Hawkes Bay 1.36874638 0.700803816 2.036688948 0.0000000
## Whangarei-Hawkes Bay 0.51433870 -0.271627182 1.300304592 0.5824478
## Northland-Marlborough -0.85473963 -1.437942857 -0.271536393 0.0001401
## Otago-Marlborough 0.24411514 -0.456078927 0.944309200 0.9920654
## Poverty Bay-Marlborough -1.79350247 -2.523687719 -1.063317214 0.0000000
## Southland-Marlborough -0.83398733 -1.534181392 -0.133793265 0.0060192
## Tasman-Marlborough -1.38755241 -1.881114732 -0.893990091 0.0000000
## Wairarapa-Marlborough -0.12904705 -0.829241112 0.571147015 0.9999807
## Whangarei-Marlborough -0.98345473 -1.797006710 -0.169902741 0.0048339
## Otago-Northland 1.09885476 0.366574281 1.831135241 0.0000823
## Poverty Bay-Northland -0.93876284 -1.699771039 -0.177754644 0.0035407
## Southland-Northland 0.02075230 -0.711528183 0.753032776 1.0000000
## Tasman-Northland -0.53281279 -1.070926844 0.005301271 0.0550488
## Wairarapa-Northland 0.72569258 -0.006587903 1.457973057 0.0545940
## Whangarei-Northland -0.12871510 -0.970041319 0.712611118 0.9999972
## Poverty Bay-Otago -2.03761760 -2.891596053 -1.183639153 0.0000000
## Southland-Otago -1.07810246 -1.906583253 -0.249621676 0.0014592
## Tasman-Otago -1.63166755 -2.294775725 -0.968559371 0.0000000
## Wairarapa-Otago -0.37316218 -1.201642973 0.455318604 0.9438252
## Whangarei-Otago -1.22756986 -2.153839543 -0.301300181 0.0010538
## Southland-Poverty Bay 0.95951514 0.105536688 1.813493588 0.0133949
## Tasman-Poverty Bay 0.40595005 -0.288752206 1.100652316 0.7421455
## Wairarapa-Poverty Bay 1.66445542 0.810476968 2.518433869 0.0000000
## Whangarei-Poverty Bay 0.81004774 -0.139096241 1.759191722 0.1803585
## Tasman-Southland -0.55356508 -1.216673261 0.109543094 0.2072837
## Wairarapa-Southland 0.70494028 -0.123540509 1.533421069 0.1839101
## Whangarei-Southland -0.14946740 -1.075737078 0.776802283 0.9999951
## Wairarapa-Tasman 1.25850536 0.595397186 1.921613541 0.0000001
## Whangarei-Tasman 0.40409769 -0.377763912 1.185959284 0.8655156
## Whangarei-Wairarapa -0.85440768 -1.780677358 0.071862003 0.1026926#impact of crop and region
shannondiv_aov <-aov(shannondiv ~ Crop+Region, data = meta_data_day1)
summary(shannondiv_aov)## Df Sum Sq Mean Sq F value Pr(>F)
## Crop 7 115.38 16.483 74.58 <2e-16 ***
## Region 11 5.20 0.473 2.14 0.018 *
## Residuals 277 61.22 0.221
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(shannondiv_aov)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = shannondiv ~ Crop + Region, data = meta_data_day1)
##
## $Crop
## diff lwr upr p adj
## Avocado-Apple 0.86043039 0.49192752 1.228933268 0.0000000
## Carrot-Apple 1.79680237 1.43640148 2.157203270 0.0000000
## Kiwifruit-Apple 0.40254673 0.03735248 0.767740982 0.0193847
## Onion-Apple 1.82081378 1.45345452 2.188173037 0.0000000
## Pak Choi-Apple 1.21163937 0.83216007 1.591118671 0.0000000
## Pear-Apple 0.43453680 -0.05636766 0.925441263 0.1258570
## Radish-Apple 0.92837784 0.39814042 1.458615258 0.0000051
## Carrot-Avocado 0.93637198 0.65664230 1.216101660 0.0000000
## Kiwifruit-Avocado -0.45788366 -0.74376254 -0.172004791 0.0000461
## Onion-Avocado 0.96038338 0.67174396 1.249022801 0.0000000
## Pak Choi-Avocado 0.35120898 0.04729381 0.655124141 0.0113242
## Pear-Avocado -0.42589359 -0.86103389 0.009246704 0.0600071
## Radish-Avocado 0.06794745 -0.41112625 0.547021144 0.9998678
## Kiwifruit-Carrot -1.39425564 -1.66961208 -1.118899205 0.0000000
## Onion-Carrot 0.02401140 -0.25421000 0.302232812 0.9999955
## Pak Choi-Carrot -0.58516300 -0.87920189 -0.291124118 0.0000001
## Pear-Carrot -1.36226557 -1.79056630 -0.933964841 0.0000000
## Radish-Carrot -0.86842453 -1.34129454 -0.395554524 0.0000014
## Onion-Kiwifruit 1.41826705 1.13386383 1.702670259 0.0000000
## Pak Choi-Kiwifruit 0.80909264 0.50919782 1.108987456 0.0000000
## Pear-Kiwifruit 0.03199007 -0.40035186 0.464332009 0.9999985
## Radish-Kiwifruit 0.52583111 0.04929771 1.002364509 0.0191635
## Pak Choi-Onion -0.60917441 -0.91170190 -0.306646912 0.0000001
## Pear-Onion -1.38627697 -1.82044921 -0.952104734 0.0000000
## Radish-Onion -0.89243594 -1.37063053 -0.414241348 0.0000009
## Pear-Pak Choi -0.77710257 -1.22157669 -0.332628442 0.0000053
## Radish-Pak Choi -0.28326153 -0.77082874 0.204305675 0.6379397
## Radish-Pear 0.49384104 -0.08469542 1.072377491 0.1577551
##
## $Region
## diff lwr upr p adj
## Bay of Plenty-Auckland 0.495349858 -0.3111288 1.3018285 0.6762565
## Canterbury-Auckland 0.504987003 -0.2846128 1.2945868 0.6174062
## Hawkes Bay-Auckland 0.473352001 -0.3515278 1.2982318 0.7635964
## Marlborough-Auckland 0.450197851 -0.3952215 1.2956172 0.8403438
## Northland-Auckland 0.555841609 -0.3104554 1.4221386 0.6124883
## Otago-Auckland 0.704984722 -0.2262566 1.6362261 0.3472492
## Poverty Bay-Auckland 0.074962431 -0.8740184 1.0239433 1.0000000
## Southland-Auckland 0.225384930 -0.7058564 1.1566263 0.9997088
## Tasman-Auckland 0.571552271 -0.2502893 1.3933938 0.4845146
## Wairarapa-Auckland 0.321150803 -0.6100905 1.2523921 0.9927358
## Whangarei-Auckland 0.427126508 -0.5731871 1.4274402 0.9613235
## Canterbury-Bay of Plenty 0.009637145 -0.2607751 0.2800494 1.0000000
## Hawkes Bay-Bay of Plenty -0.021997857 -0.3826661 0.3386704 1.0000000
## Marlborough-Bay of Plenty -0.045152007 -0.4506009 0.3602969 0.9999999
## Northland-Bay of Plenty 0.060491751 -0.3868621 0.5078456 0.9999992
## Otago-Bay of Plenty 0.209634864 -0.3532731 0.7725428 0.9863545
## Poverty Bay-Bay of Plenty -0.420387427 -1.0121810 0.1714061 0.4498113
## Southland-Bay of Plenty -0.269964929 -0.8328729 0.2929430 0.9147033
## Tasman-Bay of Plenty 0.076202413 -0.2774619 0.4298667 0.9999067
## Wairarapa-Bay of Plenty -0.174199056 -0.7371070 0.3887089 0.9971480
## Whangarei-Bay of Plenty -0.068223350 -0.7392542 0.6028074 1.0000000
## Hawkes Bay-Canterbury -0.031635002 -0.3527917 0.2895216 1.0000000
## Marlborough-Canterbury -0.054789152 -0.4255301 0.3159518 0.9999980
## Northland-Canterbury 0.050854606 -0.3653010 0.4670102 0.9999997
## Otago-Canterbury 0.199997719 -0.3384495 0.7384449 0.9866396
## Poverty Bay-Canterbury -0.430024572 -0.9986014 0.1385523 0.3487423
## Southland-Canterbury -0.279602073 -0.8180493 0.2588451 0.8615896
## Tasman-Canterbury 0.066565268 -0.2467053 0.3798358 0.9999187
## Wairarapa-Canterbury -0.183836200 -0.7222834 0.3546110 0.9933241
## Whangarei-Canterbury -0.077860495 -0.7285081 0.5727872 0.9999998
## Marlborough-Hawkes Bay -0.023154150 -0.4640709 0.4177626 1.0000000
## Northland-Hawkes Bay 0.082489608 -0.3972440 0.5622232 0.9999905
## Otago-Hawkes Bay 0.231632721 -0.3573360 0.8206015 0.9790554
## Poverty Bay-Hawkes Bay -0.398389570 -1.0150243 0.2182452 0.6019657
## Southland-Hawkes Bay -0.247967072 -0.8369358 0.3410017 0.9650781
## Tasman-Hawkes Bay 0.098200270 -0.2956236 0.4920241 0.9996121
## Wairarapa-Hawkes Bay -0.152201199 -0.7411699 0.4367675 0.9994550
## Whangarei-Hawkes Bay -0.046225493 -0.7392631 0.6468121 1.0000000
## Northland-Marlborough 0.105643758 -0.4086048 0.6198923 0.9999420
## Otago-Marlborough 0.254786871 -0.3626201 0.8721939 0.9698610
## Poverty Bay-Marlborough -0.375235420 -1.0190876 0.2686168 0.7451227
## Southland-Marlborough -0.224812921 -0.8422199 0.3925941 0.9886201
## Tasman-Marlborough 0.121354420 -0.3138518 0.5565607 0.9988895
## Wairarapa-Marlborough -0.129047048 -0.7464540 0.4883599 0.9999310
## Whangarei-Marlborough -0.023071343 -0.7404334 0.6942908 1.0000000
## Otago-Northland 0.149143113 -0.4965566 0.7948428 0.9998150
## Poverty Bay-Northland -0.480879178 -1.1519100 0.1901516 0.4354502
## Southland-Northland -0.330456679 -0.9761564 0.3152430 0.8728433
## Tasman-Northland 0.015710662 -0.4587798 0.4902011 1.0000000
## Wairarapa-Northland -0.234690806 -0.8803905 0.4110089 0.9887859
## Whangarei-Northland -0.128715101 -0.8705676 0.6131374 0.9999896
## Poverty Bay-Otago -0.630022291 -1.3830311 0.1229865 0.2043451
## Southland-Otago -0.479599793 -1.2101256 0.2509260 0.5771100
## Tasman-Otago -0.133432451 -0.7181384 0.4512735 0.9998356
## Wairarapa-Otago -0.383833919 -1.1143597 0.3466919 0.8519404
## Whangarei-Otago -0.277858214 -1.0946109 0.5388945 0.9935230
## Southland-Poverty Bay 0.150422499 -0.6025863 0.9034313 0.9999562
## Tasman-Poverty Bay 0.496589840 -0.1159747 1.1091543 0.2458941
## Wairarapa-Poverty Bay 0.246188372 -0.5068204 0.9991971 0.9953884
## Whangarei-Poverty Bay 0.352164077 -0.4847584 1.1890865 0.9652197
## Tasman-Southland 0.346167341 -0.2385386 0.9308733 0.7254765
## Wairarapa-Southland 0.095765873 -0.6347599 0.8262917 0.9999994
## Whangarei-Southland 0.201741579 -0.6150111 1.0184943 0.9996457
## Wairarapa-Tasman -0.250401468 -0.8351074 0.3343045 0.9604954
## Whangarei-Tasman -0.144425763 -0.8338444 0.5449928 0.9999295
## Whangarei-Wairarapa 0.105975705 -0.7107770 0.9227284 0.9999995#region significant in shannon
#region within a crop
#I did this for beta and then decided I prefer it because of the biased sampling across crops/regions
#radish has only one location
categories_crop <- meta_data_day1 %>% filter(Crop!='Radish') %>% select(Crop) %>% unique()
categories_crop$Crop <- gsub("Pak Choi", "Pakchoi", categories_crop$Crop)
for (item in categories_crop$Crop){
print(paste('Analysing',item))
#filtered_sppr <- sppr[grepl('^Apple', names(sppr))]
filtered_shannondiv <- shannondiv[grepl(paste0('^',item), names(shannondiv))]
filtered_meta_data_day1<-meta_data_day1 %>% filter(sample_ID %in% names(filtered_shannondiv))
shannondiv_aov <-aov(filtered_shannondiv ~ Region, data = filtered_meta_data_day1)
print(summary(shannondiv_aov))
print(TukeyHSD(shannondiv_aov))
tukey_test<-TukeyHSD(shannondiv_aov)
tukey_test<-tukey_test$Region
write.csv(tukey_test,paste('Alpha_shannon_diversity_onerep_',item,'vsregion.csv'),quote = F,row.names = T)
}## [1] "Analysing Apple"
## Df Sum Sq Mean Sq F value Pr(>F)
## Region 1 0.0856 0.08557 0.62 0.44
## Residuals 20 2.7591 0.13796
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = filtered_shannondiv ~ Region, data = filtered_meta_data_day1)
##
## $Region
## diff lwr upr p adj
## Tasman-Hawkes Bay 0.1296457 -0.2137365 0.4730279 0.4401851
##
## [1] "Analysing Pear"
## Df Sum Sq Mean Sq F value Pr(>F)
## Region 1 1.425 1.425 4.718 0.0506 .
## Residuals 12 3.624 0.302
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = filtered_shannondiv ~ Region, data = filtered_meta_data_day1)
##
## $Region
## diff lwr upr p adj
## Tasman-Hawkes Bay -0.7062287 -1.414635 0.002177221 0.0506029
##
## [1] "Analysing Avocado"
## Df Sum Sq Mean Sq F value Pr(>F)
## Region 2 0.144 0.0722 0.266 0.768
## Residuals 46 12.506 0.2719
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = filtered_shannondiv ~ Region, data = filtered_meta_data_day1)
##
## $Region
## diff lwr upr p adj
## Northland-Bay of Plenty 0.11222676 -0.2861694 0.5106229 0.7749744
## Whangarei-Bay of Plenty -0.01648834 -0.5864193 0.5534426 0.9972974
## Whangarei-Northland -0.12871510 -0.7332181 0.4757879 0.8641069
##
## [1] "Analysing Kiwifruit"
## Df Sum Sq Mean Sq F value Pr(>F)
## Region 3 3.713 1.2377 4.285 0.00929 **
## Residuals 48 13.866 0.2889
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = filtered_shannondiv ~ Region, data = filtered_meta_data_day1)
##
## $Region
## diff lwr upr p adj
## Hawkes Bay-Bay of Plenty -0.40291656 -0.97280687 0.1669738 0.2494385
## Poverty Bay-Bay of Plenty -0.48690386 -1.08120411 0.1073964 0.1433383
## Tasman-Bay of Plenty 0.22664160 -0.26689789 0.7201811 0.6160484
## Poverty Bay-Hawkes Bay -0.08398731 -0.77904325 0.6110686 0.9883561
## Tasman-Hawkes Bay 0.62955816 0.01841888 1.2406974 0.0412246
## Tasman-Poverty Bay 0.71354546 0.07958251 1.3475084 0.0217384
##
## [1] "Analysing Pakchoi"
## Df Sum Sq Mean Sq F value Pr(>F)
## Region 3 2.433 0.8110 3.332 0.0298 *
## Residuals 37 9.007 0.2434
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = filtered_shannondiv ~ Region, data = filtered_meta_data_day1)
##
## $Region
## diff lwr upr p adj
## Canterbury-Auckland 0.5910218 -0.12363480 1.3056784 0.1354441
## Hawkes Bay-Auckland 0.9346287 -0.07894837 1.9482058 0.0798167
## Southland-Auckland 0.2253849 -0.57209327 1.0228631 0.8717124
## Hawkes Bay-Canterbury 0.3436070 -0.46725473 1.1544686 0.6676674
## Southland-Canterbury -0.3656368 -0.88151436 0.1502407 0.2429283
## Southland-Hawkes Bay -0.7092438 -1.59396644 0.1754788 0.1546168
##
## [1] "Analysing Onion"
## Df Sum Sq Mean Sq F value Pr(>F)
## Region 3 0.495 0.1652 0.909 0.444
## Residuals 46 8.359 0.1817
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = filtered_shannondiv ~ Region, data = filtered_meta_data_day1)
##
## $Region
## diff lwr upr p adj
## Marlborough-Canterbury -0.09754088 -0.4816738 0.2865921 0.9053416
## Otago-Canterbury 0.12255610 -0.4642167 0.7093289 0.9442408
## Wairarapa-Canterbury -0.22658792 -0.7056859 0.2525100 0.5921865
## Otago-Marlborough 0.22009698 -0.3453314 0.7855254 0.7284405
## Wairarapa-Marlborough -0.12904705 -0.5817521 0.3236580 0.8719835
## Wairarapa-Otago -0.34914403 -0.9829310 0.2846430 0.4645013
##
## [1] "Analysing Carrot"
## Df Sum Sq Mean Sq F value Pr(>F)
## Region 1 0.314 0.3139 2.811 0.0993 .
## Residuals 55 6.141 0.1116
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = filtered_shannondiv ~ Region, data = filtered_meta_data_day1)
##
## $Region
## diff lwr upr p adj
## Otago-Canterbury 0.2904911 -0.05672469 0.6377069 0.0992879#impact of flowering time
shannondiv_aov <-aov(shannondiv ~ flowertime, data = meta_data_day1)
summary(shannondiv_aov)## Df Sum Sq Mean Sq F value Pr(>F)
## flowertime 1 87.18 87.18 270.9 <2e-16 ***
## Residuals 294 94.62 0.32
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#TukeyHSD(shannondiv_aov)
#can plot that out
shannondiv_df <- shannondiv %>%
enframe() %>%
full_join(meta_data_day1, by = c("name" = "sample_ID"))
#shannondiv_df$Crop <- factor(shannondiv_df$Crop, levels = c("Apple","Pear","Avocado","Kiwifruit","Pak Choi","Carrot", "Onion","Radish"))
shannondiv_df$Crop <- factor(shannondiv_df$Crop, levels = c("Avocado","Apple","Pear","Kiwifruit","Pak Choi","Radish", "Carrot","Onion"))
#just crop
plot_sppr <- ggplot(shannondiv_df, aes(x = Crop, y = value, fill = Crop)) +
geom_boxplot() +
scale_fill_manual(values=col_bound)+
theme_bw()+
labs(x = "Crop",
y = "Shannon",
title = "Species richness: shannon")
plot_sppr
#other significant factor was region
plot_sppr <- ggplot(shannondiv_df, aes(x = Region, y = value, fill = Crop)) +
geom_boxplot() +
facet_wrap(~Crop, scales = "free_x")+
theme_bw()+
scale_fill_manual(values=col_bound)+
labs(x = "Region",
y = "Shannon",
title = "Species richness: shannon")+
theme(axis.text.x = element_text(angle = 90,size=7,hjust=1))
plot_sppr
#other significant factor was flowertime
plot_sppr <- ggplot(shannondiv_df, aes(x = Crop, y = value, fill = Crop)) +
geom_boxplot() +
facet_wrap(~flowertime, scales = "free_x")+
theme_bw()+
scale_fill_manual(values=col_bound)+
labs(x = "Region",
y = "Shannon",
title = "Species richness: shannon")+
theme(axis.text.x = element_text(angle = 90,size=7,hjust=1))
plot_sppr
Beta diversity and MDS plots
################
#beta diversity#
################
#im not sure that this is useful as I have to remove the 0's (e.g. samples where there was no non-bee insects observed ~mostly apple/pear) but here it is:
#remove zero values:
counts_day1_wide_no0 <- counts_day1_wide[rowSums(counts_day1_wide == 0) != ncol(counts_day1_wide), ]
meta_data_day1_no0<-meta_data_day1 %>% filter(sample_ID %in% row.names(counts_day1_wide_no0))
#crop
perm<-adonis2(counts_day1_wide_no0 ~ Crop,data=meta_data_day1_no0)
perm## Permutation test for adonis under reduced model
## Permutation: free
## Number of permutations: 999
##
## adonis2(formula = counts_day1_wide_no0 ~ Crop, data = meta_data_day1_no0)
## Df SumOfSqs R2 F Pr(>F)
## Model 7 27.915 0.2488 12.917 0.001 ***
## Residual 273 84.283 0.7512
## Total 280 112.198 1.0000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1pairwise_adonis<-pairwise.adonis2(counts_day1_wide_no0 ~ Crop, data = meta_data_day1_no0)
pairwise_adonis## $parent_call
## [1] "counts_day1_wide_no0 ~ Crop , strata = Null , permutations 999"
##
## $Apple_vs_Pear
## Df SumOfSqs R2 F Pr(>F)
## Model 1 0.5977 0.0578 1.4722 0.119
## Residual 24 9.7440 0.9422
## Total 25 10.3417 1.0000
##
## $Apple_vs_Avocado
## Df SumOfSqs R2 F Pr(>F)
## Model 1 2.637 0.10987 7.7762 0.001 ***
## Residual 63 21.364 0.89013
## Total 64 24.001 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $Apple_vs_Kiwifruit
## Df SumOfSqs R2 F Pr(>F)
## Model 1 1.0005 0.04124 2.624 0.004 **
## Residual 61 23.2577 0.95876
## Total 62 24.2582 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $`Apple_vs_Pak Choi`
## Df SumOfSqs R2 F Pr(>F)
## Model 1 3.0527 0.13808 8.8108 0.001 ***
## Residual 55 19.0557 0.86192
## Total 56 22.1084 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $Apple_vs_Radish
## Df SumOfSqs R2 F Pr(>F)
## Model 1 1.4085 0.12598 3.6035 0.001 ***
## Residual 25 9.7718 0.87402
## Total 26 11.1803 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $Apple_vs_Onion
## Df SumOfSqs R2 F Pr(>F)
## Model 1 3.5847 0.15349 11.604 0.001 ***
## Residual 64 19.7698 0.84651
## Total 65 23.3544 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $Apple_vs_Carrot
## Df SumOfSqs R2 F Pr(>F)
## Model 1 4.0486 0.1703 14.573 0.001 ***
## Residual 71 19.7254 0.8297
## Total 72 23.7740 1.0000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $Pear_vs_Avocado
## Df SumOfSqs R2 F Pr(>F)
## Model 1 1.2763 0.06518 3.9742 0.001 ***
## Residual 57 18.3060 0.93482
## Total 58 19.5824 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $Pear_vs_Kiwifruit
## Df SumOfSqs R2 F Pr(>F)
## Model 1 1.1381 0.05334 3.0988 0.003 **
## Residual 55 20.1999 0.94666
## Total 56 21.3380 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $`Pear_vs_Pak Choi`
## Df SumOfSqs R2 F Pr(>F)
## Model 1 2.3829 0.12964 7.2987 0.001 ***
## Residual 49 15.9979 0.87036
## Total 50 18.3808 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $Pear_vs_Radish
## Df SumOfSqs R2 F Pr(>F)
## Model 1 1.1974 0.15135 3.3885 0.001 ***
## Residual 19 6.7140 0.84865
## Total 20 7.9114 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $Pear_vs_Onion
## Df SumOfSqs R2 F Pr(>F)
## Model 1 2.5543 0.13258 8.8649 0.001 ***
## Residual 58 16.7120 0.86742
## Total 59 19.2663 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $Pear_vs_Carrot
## Df SumOfSqs R2 F Pr(>F)
## Model 1 2.7001 0.13941 10.53 0.001 ***
## Residual 65 16.6676 0.86059
## Total 66 19.3676 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $Avocado_vs_Kiwifruit
## Df SumOfSqs R2 F Pr(>F)
## Model 1 4.843 0.1321 14.307 0.001 ***
## Residual 94 31.820 0.8679
## Total 95 36.663 1.0000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $`Avocado_vs_Pak Choi`
## Df SumOfSqs R2 F Pr(>F)
## Model 1 6.639 0.1938 21.155 0.001 ***
## Residual 88 27.618 0.8062
## Total 89 34.257 1.0000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $Avocado_vs_Radish
## Df SumOfSqs R2 F Pr(>F)
## Model 1 2.595 0.12399 8.2096 0.001 ***
## Residual 58 18.334 0.87601
## Total 59 20.929 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $Avocado_vs_Onion
## Df SumOfSqs R2 F Pr(>F)
## Model 1 7.515 0.20964 25.728 0.001 ***
## Residual 97 28.332 0.79036
## Total 98 35.847 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $Avocado_vs_Carrot
## Df SumOfSqs R2 F Pr(>F)
## Model 1 7.168 0.20218 26.355 0.001 ***
## Residual 104 28.287 0.79782
## Total 105 35.456 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $`Kiwifruit_vs_Pak Choi`
## Df SumOfSqs R2 F Pr(>F)
## Model 1 5.812 0.16453 16.936 0.001 ***
## Residual 86 29.512 0.83547
## Total 87 35.323 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $Kiwifruit_vs_Radish
## Df SumOfSqs R2 F Pr(>F)
## Model 1 1.606 0.07355 4.4461 0.001 ***
## Residual 56 20.228 0.92645
## Total 57 21.834 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $Kiwifruit_vs_Onion
## Df SumOfSqs R2 F Pr(>F)
## Model 1 7.291 0.19434 22.916 0.001 ***
## Residual 95 30.226 0.80566
## Total 96 37.517 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $Kiwifruit_vs_Carrot
## Df SumOfSqs R2 F Pr(>F)
## Model 1 8.642 0.2226 29.207 0.001 ***
## Residual 102 30.181 0.7774
## Total 103 38.824 1.0000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $`Pak Choi_vs_Radish`
## Df SumOfSqs R2 F Pr(>F)
## Model 1 0.7268 0.04338 2.2675 0.016 *
## Residual 50 16.0257 0.95662
## Total 51 16.7524 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $`Pak Choi_vs_Onion`
## Df SumOfSqs R2 F Pr(>F)
## Model 1 3.5144 0.11898 12.019 0.001 ***
## Residual 89 26.0237 0.88102
## Total 90 29.5380 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $`Pak Choi_vs_Carrot`
## Df SumOfSqs R2 F Pr(>F)
## Model 1 4.508 0.14787 16.658 0.001 ***
## Residual 96 25.979 0.85213
## Total 97 30.487 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $Radish_vs_Onion
## Df SumOfSqs R2 F Pr(>F)
## Model 1 1.6406 0.08926 5.7823 0.001 ***
## Residual 59 16.7398 0.91074
## Total 60 18.3804 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $Radish_vs_Carrot
## Df SumOfSqs R2 F Pr(>F)
## Model 1 1.8926 0.10182 7.4817 0.001 ***
## Residual 66 16.6954 0.89818
## Total 67 18.5879 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $Onion_vs_Carrot
## Df SumOfSqs R2 F Pr(>F)
## Model 1 2.5064 0.08584 9.8591 0.001 ***
## Residual 105 26.6933 0.91416
## Total 106 29.1998 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## attr(,"class")
## [1] "pwadstrata" "list"tests<-names(pairwise_adonis)
tests<-tests[-1]
columns= c("Test","pvalue")
myData = data.frame(matrix(nrow = 0, ncol = length(columns)))
for (item in tests){
# print(item)
pvalue<-eval(parse(text =paste0('pairwise_adonis$','`',item,'`' ,'$`Pr(>F)`[1]')))
# print(pvalue)
out<-data.frame(Test = item, pvalue = pvalue)
#print(out)
myData<-rbind(myData,out)
}
write.csv(myData,'Pairwise_adonis_beta_diversity_day1repsonly_countsvscrops.csv',quote = F,row.names = F)
#flowertime
perm<-adonis2(counts_day1_wide_no0 ~ flowertime,data=meta_data_day1_no0)
perm## Permutation test for adonis under reduced model
## Permutation: free
## Number of permutations: 999
##
## adonis2(formula = counts_day1_wide_no0 ~ flowertime, data = meta_data_day1_no0)
## Df SumOfSqs R2 F Pr(>F)
## Model 1 12.753 0.11366 35.779 0.001 ***
## Residual 279 99.445 0.88634
## Total 280 112.198 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#pairwise_adonis<-pairwise.adonis2(counts_day1_wide_no0 ~ flowertime, data = meta_data_day1_no0)
#pairwise_adonis
#I think to look at the impact of region within a crop we need to run this within a crop. This is because there is little overlap in regions between crops and the 0,0 situation makes it too confusing (at least for me)
#so just testing does community composition vary between regions within a crop.
categories_crop <- meta_data_day1 %>% filter(Crop!='Radish') %>% select(Crop) %>% unique()
categories_crop$Crop <- gsub("Pak Choi", "Pakchoi", categories_crop$Crop)
for (item in categories_crop$Crop){
print(paste('Analysing',item))
filtered_counts_day1_wide_no0 <- counts_day1_wide_no0[grepl(paste0('^',item), rownames(counts_day1_wide_no0)), ] %>% select(where(~ sum(.) != 0))
filtered_meta_data_day1_no0<-meta_data_day1 %>% filter(sample_ID %in% row.names(filtered_counts_day1_wide_no0))
perm<-adonis2(filtered_counts_day1_wide_no0 ~ Region,data=filtered_meta_data_day1_no0)
print(perm)
pairwise_adonis<-pairwise.adonis2(filtered_counts_day1_wide_no0 ~ Region, data = filtered_meta_data_day1_no0)
print(pairwise_adonis)
tests<-names(pairwise_adonis)
tests<-tests[-1]
columns= c("Test","pvalue")
myData = data.frame(matrix(nrow = 0, ncol = length(columns)))
for (item1 in tests){
# print(item)
pvalue<-eval(parse(text =paste0('pairwise_adonis$','`',item1,'`' ,'$`Pr(>F)`[1]')))
# print(pvalue)
out<-data.frame(Test = item1, pvalue = pvalue)
#print(out)
myData<-rbind(myData,out)
}
write.csv(myData,paste0('Pairwise_adonis_beta_diversity_day1repsonly_regionvs',item,'.csv'),quote = F,row.names = F)
}## [1] "Analysing Apple"
## Permutation test for adonis under reduced model
## Permutation: free
## Number of permutations: 999
##
## adonis2(formula = filtered_counts_day1_wide_no0 ~ Region, data = filtered_meta_data_day1_no0)
## Df SumOfSqs R2 F Pr(>F)
## Model 1 1.1610 0.18138 3.102 0.002 **
## Residual 14 5.2399 0.81862
## Total 15 6.4009 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## $parent_call
## [1] "filtered_counts_day1_wide_no0 ~ Region , strata = Null , permutations 999"
##
## $`Hawkes Bay_vs_Tasman`
## Df SumOfSqs R2 F Pr(>F)
## Model 1 1.1610 0.18138 3.102 0.001 ***
## Residual 14 5.2399 0.81862
## Total 15 6.4009 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## attr(,"class")
## [1] "pwadstrata" "list"
## [1] "Analysing Pear"
## Permutation test for adonis under reduced model
## Permutation: free
## Number of permutations: 999
##
## adonis2(formula = filtered_counts_day1_wide_no0 ~ Region, data = filtered_meta_data_day1_no0)
## Df SumOfSqs R2 F Pr(>F)
## Model 1 0.7951 0.23784 2.4965 0.006 **
## Residual 8 2.5480 0.76216
## Total 9 3.3431 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## $parent_call
## [1] "filtered_counts_day1_wide_no0 ~ Region , strata = Null , permutations 999"
##
## $`Hawkes Bay_vs_Tasman`
## Df SumOfSqs R2 F Pr(>F)
## Model 1 0.7951 0.23784 2.4965 0.006 **
## Residual 8 2.5480 0.76216
## Total 9 3.3431 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## attr(,"class")
## [1] "pwadstrata" "list"
## [1] "Analysing Avocado"
## Permutation test for adonis under reduced model
## Permutation: free
## Number of permutations: 999
##
## adonis2(formula = filtered_counts_day1_wide_no0 ~ Region, data = filtered_meta_data_day1_no0)
## Df SumOfSqs R2 F Pr(>F)
## Model 2 1.5319 0.10238 2.6232 0.002 **
## Residual 46 13.4311 0.89762
## Total 48 14.9629 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## $parent_call
## [1] "filtered_counts_day1_wide_no0 ~ Region , strata = Null , permutations 999"
##
## $`Bay of Plenty_vs_Northland`
## Df SumOfSqs R2 F Pr(>F)
## Model 1 1.0994 0.0823 3.6767 0.001 ***
## Residual 41 12.2601 0.9177
## Total 42 13.3596 1.0000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $`Bay of Plenty_vs_Whangarei`
## Df SumOfSqs R2 F Pr(>F)
## Model 1 0.3421 0.03473 1.1155 0.332
## Residual 31 9.5070 0.96527
## Total 32 9.8491 1.00000
##
## $Northland_vs_Whangarei
## Df SumOfSqs R2 F Pr(>F)
## Model 1 0.7498 0.12829 2.9434 0.003 **
## Residual 20 5.0950 0.87171
## Total 21 5.8448 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## attr(,"class")
## [1] "pwadstrata" "list"
## [1] "Analysing Kiwifruit"
## Permutation test for adonis under reduced model
## Permutation: free
## Number of permutations: 999
##
## adonis2(formula = filtered_counts_day1_wide_no0 ~ Region, data = filtered_meta_data_day1_no0)
## Df SumOfSqs R2 F Pr(>F)
## Model 3 2.4743 0.14678 2.4658 0.001 ***
## Residual 43 14.3825 0.85322
## Total 46 16.8568 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## $parent_call
## [1] "filtered_counts_day1_wide_no0 ~ Region , strata = Null , permutations 999"
##
## $`Bay of Plenty_vs_Poverty Bay`
## Df SumOfSqs R2 F Pr(>F)
## Model 1 0.3823 0.03951 0.9873 0.427
## Residual 24 9.2930 0.96049
## Total 25 9.6753 1.00000
##
## $`Bay of Plenty_vs_Hawkes Bay`
## Df SumOfSqs R2 F Pr(>F)
## Model 1 0.9476 0.09237 2.5442 0.011 *
## Residual 25 9.3112 0.90763
## Total 26 10.2588 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $`Bay of Plenty_vs_Tasman`
## Df SumOfSqs R2 F Pr(>F)
## Model 1 0.9537 0.08065 2.8073 0.008 **
## Residual 32 10.8714 0.91935
## Total 33 11.8251 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $`Poverty Bay_vs_Hawkes Bay`
## Df SumOfSqs R2 F Pr(>F)
## Model 1 0.9114 0.20608 2.8553 0.036 *
## Residual 11 3.5112 0.79392
## Total 12 4.4226 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $`Poverty Bay_vs_Tasman`
## Df SumOfSqs R2 F Pr(>F)
## Model 1 0.6616 0.1154 2.3482 0.034 *
## Residual 18 5.0713 0.8846
## Total 19 5.7329 1.0000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $`Hawkes Bay_vs_Tasman`
## Df SumOfSqs R2 F Pr(>F)
## Model 1 1.1077 0.17874 4.1351 0.001 ***
## Residual 19 5.0895 0.82126
## Total 20 6.1972 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## attr(,"class")
## [1] "pwadstrata" "list"
## [1] "Analysing Pakchoi"
## Permutation test for adonis under reduced model
## Permutation: free
## Number of permutations: 999
##
## adonis2(formula = filtered_counts_day1_wide_no0 ~ Region, data = filtered_meta_data_day1_no0)
## Df SumOfSqs R2 F Pr(>F)
## Model 3 2.5275 0.19973 3.0781 0.001 ***
## Residual 37 10.1273 0.80027
## Total 40 12.6548 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## Set of permutations < 'minperm'. Generating entire set.## $parent_call
## [1] "filtered_counts_day1_wide_no0 ~ Region , strata = Null , permutations 999"
##
## $Auckland_vs_Canterbury
## Df SumOfSqs R2 F Pr(>F)
## Model 1 1.1250 0.13494 4.2115 0.001 ***
## Residual 27 7.2122 0.86506
## Total 28 8.3372 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $`Auckland_vs_Hawkes Bay`
## Df SumOfSqs R2 F Pr(>F)
## Model 1 0.3954 0.22901 1.4852 0.249
## Residual 5 1.3312 0.77099
## Total 6 1.7265 1.00000
##
## $Auckland_vs_Southland
## Df SumOfSqs R2 F Pr(>F)
## Model 1 0.9536 0.22868 3.2612 0.003 **
## Residual 11 3.2165 0.77132
## Total 12 4.1702 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $`Canterbury_vs_Hawkes Bay`
## Df SumOfSqs R2 F Pr(>F)
## Model 1 0.5671 0.07584 2.1335 0.024 *
## Residual 26 6.9107 0.92416
## Total 27 7.4778 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $Canterbury_vs_Southland
## Df SumOfSqs R2 F Pr(>F)
## Model 1 0.9286 0.09549 3.3781 0.001 ***
## Residual 32 8.7961 0.90451
## Total 33 9.7247 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $`Hawkes Bay_vs_Southland`
## Df SumOfSqs R2 F Pr(>F)
## Model 1 0.6992 0.19346 2.3987 0.038 *
## Residual 10 2.9150 0.80654
## Total 11 3.6142 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## attr(,"class")
## [1] "pwadstrata" "list"
## [1] "Analysing Onion"
## Permutation test for adonis under reduced model
## Permutation: free
## Number of permutations: 999
##
## adonis2(formula = filtered_counts_day1_wide_no0 ~ Region, data = filtered_meta_data_day1_no0)
## Df SumOfSqs R2 F Pr(>F)
## Model 3 1.6693 0.12486 2.1877 0.001 ***
## Residual 46 11.6996 0.87514
## Total 49 13.3689 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## $parent_call
## [1] "filtered_counts_day1_wide_no0 ~ Region , strata = Null , permutations 999"
##
## $Canterbury_vs_Otago
## Df SumOfSqs R2 F Pr(>F)
## Model 1 0.4072 0.09027 1.786 0.048 *
## Residual 18 4.1038 0.90973
## Total 19 4.5110 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $Canterbury_vs_Marlborough
## Df SumOfSqs R2 F Pr(>F)
## Model 1 0.6737 0.07373 2.7063 0.004 **
## Residual 34 8.4640 0.92627
## Total 35 9.1378 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $Canterbury_vs_Wairarapa
## Df SumOfSqs R2 F Pr(>F)
## Model 1 0.6049 0.09762 2.3801 0.01 **
## Residual 22 5.5909 0.90238
## Total 23 6.1958 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $Otago_vs_Marlborough
## Df SumOfSqs R2 F Pr(>F)
## Model 1 0.3443 0.05336 1.3528 0.159
## Residual 24 6.1087 0.94664
## Total 25 6.4530 1.00000
##
## $Otago_vs_Wairarapa
## Df SumOfSqs R2 F Pr(>F)
## Model 1 0.4502 0.12215 1.6698 0.061 .
## Residual 12 3.2355 0.87785
## Total 13 3.6858 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $Marlborough_vs_Wairarapa
## Df SumOfSqs R2 F Pr(>F)
## Model 1 0.7098 0.08546 2.6166 0.007 **
## Residual 28 7.5958 0.91454
## Total 29 8.3056 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## attr(,"class")
## [1] "pwadstrata" "list"
## [1] "Analysing Carrot"
## Permutation test for adonis under reduced model
## Permutation: free
## Number of permutations: 999
##
## adonis2(formula = filtered_counts_day1_wide_no0 ~ Region, data = filtered_meta_data_day1_no0)
## Df SumOfSqs R2 F Pr(>F)
## Model 1 0.4344 0.0326 1.8535 0.04 *
## Residual 55 12.8901 0.9674
## Total 56 13.3245 1.0000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## $parent_call
## [1] "filtered_counts_day1_wide_no0 ~ Region , strata = Null , permutations 999"
##
## $Canterbury_vs_Otago
## Df SumOfSqs R2 F Pr(>F)
## Model 1 0.4344 0.0326 1.8535 0.037 *
## Residual 55 12.8901 0.9674
## Total 56 13.3245 1.0000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## attr(,"class")
## [1] "pwadstrata" "list"#MDS
pk_NMDS <- metaMDS(counts_day1_wide_no0,trymax=20)## Square root transformation
## Wisconsin double standardization
## Run 0 stress 0.1742045
## Run 1 stress 0.1741919
## ... New best solution
## ... Procrustes: rmse 0.009913288 max resid 0.05505071
## Run 2 stress 0.1738884
## ... New best solution
## ... Procrustes: rmse 0.00643423 max resid 0.04474276
## Run 3 stress 0.1739235
## ... Procrustes: rmse 0.01348199 max resid 0.09078369
## Run 4 stress 0.1740874
## ... Procrustes: rmse 0.007170677 max resid 0.05399337
## Run 5 stress 0.1738227
## ... New best solution
## ... Procrustes: rmse 0.004599909 max resid 0.03795657
## Run 6 stress 0.1741892
## ... Procrustes: rmse 0.00811584 max resid 0.06963205
## Run 7 stress 0.1738668
## ... Procrustes: rmse 0.01157723 max resid 0.0563795
## Run 8 stress 0.1748467
## Run 9 stress 0.1737296
## ... New best solution
## ... Procrustes: rmse 0.008571698 max resid 0.05197666
## Run 10 stress 0.1752747
## Run 11 stress 0.1744465
## Run 12 stress 0.1747564
## Run 13 stress 0.1739611
## ... Procrustes: rmse 0.01287711 max resid 0.06373397
## Run 14 stress 0.1740124
## ... Procrustes: rmse 0.005789706 max resid 0.03672755
## Run 15 stress 0.1747287
## Run 16 stress 0.1740435
## ... Procrustes: rmse 0.0056725 max resid 0.04662368
## Run 17 stress 0.1752499
## Run 18 stress 0.1741146
## ... Procrustes: rmse 0.0115926 max resid 0.06139293
## Run 19 stress 0.1746273
## Run 20 stress 0.1737481
## ... Procrustes: rmse 0.003671995 max resid 0.02473057
## *** Best solution was not repeated -- monoMDS stopping criteria:
## 18: no. of iterations >= maxit
## 2: stress ratio > sratmax#doesnt converge - whats up?
pk_NMDS##
## Call:
## metaMDS(comm = counts_day1_wide_no0, trymax = 20)
##
## global Multidimensional Scaling using monoMDS
##
## Data: wisconsin(sqrt(counts_day1_wide_no0))
## Distance: bray
##
## Dimensions: 2
## Stress: 0.1737296
## Stress type 1, weak ties
## Best solution was not repeated after 20 tries
## The best solution was from try 9 (random start)
## Scaling: centring, PC rotation, halfchange scaling
## Species: expanded scores based on 'wisconsin(sqrt(counts_day1_wide_no0))'#stress (ideally <0.2)
stressplot(pk_NMDS)
plot(pk_NMDS)
plot_df <- scores(pk_NMDS, display = "sites") %>%
as.data.frame() %>%
rownames_to_column("site") %>%
full_join(meta_data_day1_no0, by = c("site" = "sample_ID"))
#keep same colour scheme
#plot_df$Crop <- factor(plot_df$Crop, levels = c("Apple","Pear","Avocado","Kiwifruit","Pak Choi","Carrot", "Onion","Radish"))
plot_df$Crop <- factor(plot_df$Crop, levels = c("Avocado","Apple","Pear","Kiwifruit","Pak Choi","Radish", "Carrot","Onion"))
plot_nmds <- ggplot(plot_df, aes(x = NMDS1, y = NMDS2, color = Crop)) +
geom_point(size = 2, alpha = 0.8) +
stat_ellipse(linetype = 2, linewidth = 0.5) +
theme_bw()+
labs(title = "NMDS")
plot_nmds## Warning in MASS::cov.trob(data[, vars]): Probable convergence failure
#################################################################
#setting a minimum count of 5 across at least 2 taxa#
#################################################################
#this is a little arbitary but means the MDS plot isnt trying to converge stuff with basically no data
#remove <5 counts:
counts_day1_wide_no0 <- counts_day1_wide[rowSums(counts_day1_wide) >= 5, ]
#remove things that are only from a single species
counts_day1_wide_no0 <-counts_day1_wide_no0[which(rowSums(1*(counts_day1_wide_no0!=0))>1),]
meta_data_day1_no0<-meta_data_day1 %>% filter(sample_ID %in% row.names(counts_day1_wide_no0))
#redoing the beta as well
#crop
perm<-adonis2(counts_day1_wide_no0 ~ Crop,data=meta_data_day1_no0)
perm## Permutation test for adonis under reduced model
## Permutation: free
## Number of permutations: 999
##
## adonis2(formula = counts_day1_wide_no0 ~ Crop, data = meta_data_day1_no0)
## Df SumOfSqs R2 F Pr(>F)
## Model 7 23.880 0.26369 11.921 0.001 ***
## Residual 233 66.679 0.73631
## Total 240 90.559 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1pairwise_adonis<-pairwise.adonis2(counts_day1_wide_no0 ~ Crop, data = meta_data_day1_no0)
pairwise_adonis## $parent_call
## [1] "counts_day1_wide_no0 ~ Crop , strata = Null , permutations 999"
##
## $Apple_vs_Pear
## Df SumOfSqs R2 F Pr(>F)
## Model 1 0.27733 0.09104 0.8012 0.62
## Residual 8 2.76903 0.90896
## Total 9 3.04636 1.00000
##
## $Apple_vs_Avocado
## Df SumOfSqs R2 F Pr(>F)
## Model 1 1.0069 0.06211 3.2452 0.003 **
## Residual 49 15.2036 0.93789
## Total 50 16.2105 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $Apple_vs_Kiwifruit
## Df SumOfSqs R2 F Pr(>F)
## Model 1 0.5443 0.05616 1.6661 0.092 .
## Residual 28 9.1464 0.94384
## Total 29 9.6906 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $`Apple_vs_Pak Choi`
## Df SumOfSqs R2 F Pr(>F)
## Model 1 1.156 0.07684 3.5792 0.001 ***
## Residual 43 13.888 0.92316
## Total 44 15.044 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $Apple_vs_Radish
## Df SumOfSqs R2 F Pr(>F)
## Model 1 0.6252 0.11955 1.7652 0.062 .
## Residual 13 4.6040 0.88045
## Total 14 5.2292 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $Apple_vs_Onion
## Df SumOfSqs R2 F Pr(>F)
## Model 1 1.2963 0.08526 4.7533 0.001 ***
## Residual 51 13.9091 0.91474
## Total 52 15.2054 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $Apple_vs_Carrot
## Df SumOfSqs R2 F Pr(>F)
## Model 1 1.3765 0.08638 5.5786 0.001 ***
## Residual 59 14.5576 0.91362
## Total 60 15.9340 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $Pear_vs_Avocado
## Df SumOfSqs R2 F Pr(>F)
## Model 1 0.9155 0.05575 3.0111 0.001 ***
## Residual 51 15.5064 0.94425
## Total 52 16.4219 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $Pear_vs_Kiwifruit
## Df SumOfSqs R2 F Pr(>F)
## Model 1 1.1788 0.11092 3.7426 0.001 ***
## Residual 30 9.4491 0.88908
## Total 31 10.6280 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $`Pear_vs_Pak Choi`
## Df SumOfSqs R2 F Pr(>F)
## Model 1 1.8414 0.11486 5.8392 0.001 ***
## Residual 45 14.1907 0.88514
## Total 46 16.0321 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $Pear_vs_Radish
## Df SumOfSqs R2 F Pr(>F)
## Model 1 1.0853 0.18113 3.3179 0.002 **
## Residual 15 4.9068 0.81887
## Total 16 5.9922 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $Pear_vs_Onion
## Df SumOfSqs R2 F Pr(>F)
## Model 1 1.9054 0.11822 7.1058 0.001 ***
## Residual 53 14.2118 0.88178
## Total 54 16.1173 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $Pear_vs_Carrot
## Df SumOfSqs R2 F Pr(>F)
## Model 1 1.8738 0.11197 7.6917 0.001 ***
## Residual 61 14.8604 0.88803
## Total 62 16.7342 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $Avocado_vs_Kiwifruit
## Df SumOfSqs R2 F Pr(>F)
## Model 1 3.5813 0.14064 11.619 0.001 ***
## Residual 71 21.8837 0.85936
## Total 72 25.4651 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $`Avocado_vs_Pak Choi`
## Df SumOfSqs R2 F Pr(>F)
## Model 1 6.631 0.19938 21.417 0.001 ***
## Residual 86 26.625 0.80062
## Total 87 33.256 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $Avocado_vs_Radish
## Df SumOfSqs R2 F Pr(>F)
## Model 1 2.6249 0.13147 8.4765 0.001 ***
## Residual 56 17.3414 0.86853
## Total 57 19.9663 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $Avocado_vs_Onion
## Df SumOfSqs R2 F Pr(>F)
## Model 1 7.541 0.22059 26.604 0.001 ***
## Residual 94 26.646 0.77941
## Total 95 34.188 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $Avocado_vs_Carrot
## Df SumOfSqs R2 F Pr(>F)
## Model 1 7.058 0.20545 26.375 0.001 ***
## Residual 102 27.295 0.79455
## Total 103 34.353 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $`Kiwifruit_vs_Pak Choi`
## Df SumOfSqs R2 F Pr(>F)
## Model 1 4.5383 0.18076 14.342 0.001 ***
## Residual 65 20.5680 0.81924
## Total 66 25.1063 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $Kiwifruit_vs_Radish
## Df SumOfSqs R2 F Pr(>F)
## Model 1 1.4015 0.11048 4.3469 0.001 ***
## Residual 35 11.2841 0.88952
## Total 36 12.6856 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $Kiwifruit_vs_Onion
## Df SumOfSqs R2 F Pr(>F)
## Model 1 5.621 0.21446 19.93 0.001 ***
## Residual 73 20.589 0.78554
## Total 74 26.210 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $Kiwifruit_vs_Carrot
## Df SumOfSqs R2 F Pr(>F)
## Model 1 6.3615 0.23049 24.262 0.001 ***
## Residual 81 21.2377 0.76951
## Total 82 27.5992 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $`Pak Choi_vs_Radish`
## Df SumOfSqs R2 F Pr(>F)
## Model 1 0.7268 0.04338 2.2675 0.011 *
## Residual 50 16.0257 0.95662
## Total 51 16.7524 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $`Pak Choi_vs_Onion`
## Df SumOfSqs R2 F Pr(>F)
## Model 1 3.5642 0.12335 12.382 0.001 ***
## Residual 88 25.3307 0.87665
## Total 89 28.8950 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $`Pak Choi_vs_Carrot`
## Df SumOfSqs R2 F Pr(>F)
## Model 1 4.508 0.14787 16.658 0.001 ***
## Residual 96 25.979 0.85213
## Total 97 30.487 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $Radish_vs_Onion
## Df SumOfSqs R2 F Pr(>F)
## Model 1 1.6844 0.095 6.0881 0.001 ***
## Residual 58 16.0468 0.905
## Total 59 17.7312 1.000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $Radish_vs_Carrot
## Df SumOfSqs R2 F Pr(>F)
## Model 1 1.8926 0.10182 7.4817 0.001 ***
## Residual 66 16.6954 0.89818
## Total 67 18.5879 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $Onion_vs_Carrot
## Df SumOfSqs R2 F Pr(>F)
## Model 1 2.5059 0.08791 10.023 0.001 ***
## Residual 104 26.0004 0.91209
## Total 105 28.5063 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## attr(,"class")
## [1] "pwadstrata" "list"tests<-names(pairwise_adonis)
tests<-tests[-1]
columns= c("Test","pvalue")
myData = data.frame(matrix(nrow = 0, ncol = length(columns)))
for (item in tests){
# print(item)
pvalue<-eval(parse(text =paste0('pairwise_adonis$','`',item,'`' ,'$`Pr(>F)`[1]')))
# print(pvalue)
out<-data.frame(Test = item, pvalue = pvalue)
#print(out)
myData<-rbind(myData,out)
}
write.csv(myData,'Pairwise_adonis_beta_diversity_day1repsonly_countsvscrops_mincount5.csv',quote = F,row.names = F)
#flowertime
perm<-adonis2(counts_day1_wide_no0 ~ flowertime,data=meta_data_day1_no0)
perm## Permutation test for adonis under reduced model
## Permutation: free
## Number of permutations: 999
##
## adonis2(formula = counts_day1_wide_no0 ~ flowertime, data = meta_data_day1_no0)
## Df SumOfSqs R2 F Pr(>F)
## Model 1 10.641 0.1175 31.823 0.001 ***
## Residual 239 79.918 0.8825
## Total 240 90.559 1.0000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#pairwise_adonis<-pairwise.adonis2(counts_day1_wide_no0 ~ flowertime, data = meta_data_day1_no00)
#pairwise_adonis
#I think to look at the impact of region within a crop we need to run this within a crop. This is because there is little overlap in regions between crops and the 0,0 situation makes it too confusing (at least for me)
#so just testing does community composition vary between regions within a crop.
categories_crop <- meta_data_day1 %>% filter(Crop!='Radish') %>% select(Crop) %>% unique()
categories_crop$Crop <- gsub("Pak Choi", "Pakchoi", categories_crop$Crop)
for (item in categories_crop$Crop){
print(paste('Analysing',item))
filtered_counts_day1_wide_no0<- counts_day1_wide_no0[grepl(paste0('^',item), rownames(counts_day1_wide_no0)), ] %>% select(where(~ sum(.) != 0))
filtered_meta_data_day1_no0<-meta_data_day1 %>% filter(sample_ID %in% row.names(filtered_counts_day1_wide_no0))
perm<-adonis2(filtered_counts_day1_wide_no0 ~ Region,data=filtered_meta_data_day1_no0)
print(perm)
pairwise_adonis<-pairwise.adonis2(filtered_counts_day1_wide_no0 ~ Region, data = filtered_meta_data_day1_no0)
print(pairwise_adonis)
tests<-names(pairwise_adonis)
tests<-tests[-1]
columns= c("Test","pvalue")
myData = data.frame(matrix(nrow = 0, ncol = length(columns)))
for (item1 in tests){
# print(item)
pvalue<-eval(parse(text =paste0('pairwise_adonis$','`',item1,'`' ,'$`Pr(>F)`[1]')))
# print(pvalue)
out<-data.frame(Test = item1, pvalue = pvalue)
#print(out)
myData<-rbind(myData,out)
}
write.csv(myData,paste0('Pairwise_adonis_beta_diversity_day1repsonly_regionvs',item,'_mincount5.csv'),quote = F,row.names = F)
}## [1] "Analysing Apple"## 'nperm' >= set of all permutations: complete enumeration.
## Set of permutations < 'minperm'. Generating entire set.## Permutation test for adonis under reduced model
## Permutation: free
## Number of permutations: 23
##
## adonis2(formula = filtered_counts_day1_wide_no0 ~ Region, data = filtered_meta_data_day1_no0)
## Df SumOfSqs R2 F Pr(>F)
## Model 1 0.31711 0.25716 0.6924 1
## Residual 2 0.91602 0.74284
## Total 3 1.23312 1.00000## 'nperm' >= set of all permutations: complete enumeration.
## Set of permutations < 'minperm'. Generating entire set.## $parent_call
## [1] "filtered_counts_day1_wide_no0 ~ Region , strata = Null , permutations 999"
##
## $`Hawkes Bay_vs_Tasman`
## Df SumOfSqs R2 F Pr(>F)
## Model 1 0.31711 0.25716 0.6924 1
## Residual 2 0.91602 0.74284
## Total 3 1.23312 1.00000
##
## attr(,"class")
## [1] "pwadstrata" "list"
## [1] "Analysing Pear"## 'nperm' >= set of all permutations: complete enumeration.
## Set of permutations < 'minperm'. Generating entire set.## Permutation test for adonis under reduced model
## Permutation: free
## Number of permutations: 719
##
## adonis2(formula = filtered_counts_day1_wide_no0 ~ Region, data = filtered_meta_data_day1_no0)
## Df SumOfSqs R2 F Pr(>F)
## Model 1 0.59633 0.38826 2.5387 0.06667 .
## Residual 4 0.93958 0.61174
## Total 5 1.53591 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## 'nperm' >= set of all permutations: complete enumeration.
## Set of permutations < 'minperm'. Generating entire set.## $parent_call
## [1] "filtered_counts_day1_wide_no0 ~ Region , strata = Null , permutations 999"
##
## $`Hawkes Bay_vs_Tasman`
## Df SumOfSqs R2 F Pr(>F)
## Model 1 0.59633 0.38826 2.5387 0.06667 .
## Residual 4 0.93958 0.61174
## Total 5 1.53591 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## attr(,"class")
## [1] "pwadstrata" "list"
## [1] "Analysing Avocado"
## Permutation test for adonis under reduced model
## Permutation: free
## Number of permutations: 999
##
## adonis2(formula = filtered_counts_day1_wide_no0 ~ Region, data = filtered_meta_data_day1_no0)
## Df SumOfSqs R2 F Pr(>F)
## Model 2 1.5275 0.10934 2.7008 0.002 **
## Residual 44 12.4430 0.89066
## Total 46 13.9705 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## $parent_call
## [1] "filtered_counts_day1_wide_no0 ~ Region , strata = Null , permutations 999"
##
## $`Bay of Plenty_vs_Northland`
## Df SumOfSqs R2 F Pr(>F)
## Model 1 1.1261 0.09082 3.896 0.001 ***
## Residual 39 11.2720 0.90918
## Total 40 12.3981 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $`Bay of Plenty_vs_Whangarei`
## Df SumOfSqs R2 F Pr(>F)
## Model 1 0.3181 0.03422 1.0631 0.374
## Residual 30 8.9773 0.96578
## Total 31 9.2955 1.00000
##
## $Northland_vs_Whangarei
## Df SumOfSqs R2 F Pr(>F)
## Model 1 0.7355 0.13691 3.0139 0.003 **
## Residual 19 4.6366 0.86309
## Total 20 5.3721 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## attr(,"class")
## [1] "pwadstrata" "list"
## [1] "Analysing Kiwifruit"
## Permutation test for adonis under reduced model
## Permutation: free
## Number of permutations: 999
##
## adonis2(formula = filtered_counts_day1_wide_no0 ~ Region, data = filtered_meta_data_day1_no0)
## Df SumOfSqs R2 F Pr(>F)
## Model 3 1.8535 0.23423 2.2431 0.002 **
## Residual 22 6.0597 0.76577
## Total 25 7.9132 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## 'nperm' >= set of all permutations: complete enumeration.
## Set of permutations < 'minperm'. Generating entire set.## $parent_call
## [1] "filtered_counts_day1_wide_no0 ~ Region , strata = Null , permutations 999"
##
## $`Bay of Plenty_vs_Poverty Bay`
## Df SumOfSqs R2 F Pr(>F)
## Model 1 0.1285 0.03484 0.3971 0.975
## Residual 11 3.5586 0.96516
## Total 12 3.6870 1.00000
##
## $`Bay of Plenty_vs_Hawkes Bay`
## Df SumOfSqs R2 F Pr(>F)
## Model 1 0.5004 0.12167 1.5238 0.167
## Residual 11 3.6120 0.87833
## Total 12 4.1124 1.00000
##
## $`Bay of Plenty_vs_Tasman`
## Df SumOfSqs R2 F Pr(>F)
## Model 1 1.2604 0.18801 4.6307 0.001 ***
## Residual 20 5.4438 0.81199
## Total 21 6.7042 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $`Poverty Bay_vs_Hawkes Bay`
## Df SumOfSqs R2 F Pr(>F)
## Model 1 0.35776 0.36742 1.1616 0.6667
## Residual 2 0.61596 0.63258
## Total 3 0.97372 1.00000
##
## $`Poverty Bay_vs_Tasman`
## Df SumOfSqs R2 F Pr(>F)
## Model 1 0.52261 0.17595 2.3486 0.042 *
## Residual 11 2.44769 0.82405
## Total 12 2.97030 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $`Hawkes Bay_vs_Tasman`
## Df SumOfSqs R2 F Pr(>F)
## Model 1 0.31247 0.11106 1.3742 0.187
## Residual 11 2.50115 0.88894
## Total 12 2.81362 1.00000
##
## attr(,"class")
## [1] "pwadstrata" "list"
## [1] "Analysing Pakchoi"
## Permutation test for adonis under reduced model
## Permutation: free
## Number of permutations: 999
##
## adonis2(formula = filtered_counts_day1_wide_no0 ~ Region, data = filtered_meta_data_day1_no0)
## Df SumOfSqs R2 F Pr(>F)
## Model 3 2.5275 0.19973 3.0781 0.001 ***
## Residual 37 10.1273 0.80027
## Total 40 12.6548 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## Set of permutations < 'minperm'. Generating entire set.## $parent_call
## [1] "filtered_counts_day1_wide_no0 ~ Region , strata = Null , permutations 999"
##
## $Auckland_vs_Canterbury
## Df SumOfSqs R2 F Pr(>F)
## Model 1 1.1250 0.13494 4.2115 0.001 ***
## Residual 27 7.2122 0.86506
## Total 28 8.3372 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $`Auckland_vs_Hawkes Bay`
## Df SumOfSqs R2 F Pr(>F)
## Model 1 0.3954 0.22901 1.4852 0.225
## Residual 5 1.3312 0.77099
## Total 6 1.7265 1.00000
##
## $Auckland_vs_Southland
## Df SumOfSqs R2 F Pr(>F)
## Model 1 0.9536 0.22868 3.2612 0.004 **
## Residual 11 3.2165 0.77132
## Total 12 4.1702 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $`Canterbury_vs_Hawkes Bay`
## Df SumOfSqs R2 F Pr(>F)
## Model 1 0.5671 0.07584 2.1335 0.023 *
## Residual 26 6.9107 0.92416
## Total 27 7.4778 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $Canterbury_vs_Southland
## Df SumOfSqs R2 F Pr(>F)
## Model 1 0.9286 0.09549 3.3781 0.001 ***
## Residual 32 8.7961 0.90451
## Total 33 9.7247 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $`Hawkes Bay_vs_Southland`
## Df SumOfSqs R2 F Pr(>F)
## Model 1 0.6992 0.19346 2.3987 0.04 *
## Residual 10 2.9150 0.80654
## Total 11 3.6142 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## attr(,"class")
## [1] "pwadstrata" "list"
## [1] "Analysing Onion"
## Permutation test for adonis under reduced model
## Permutation: free
## Number of permutations: 999
##
## adonis2(formula = filtered_counts_day1_wide_no0 ~ Region, data = filtered_meta_data_day1_no0)
## Df SumOfSqs R2 F Pr(>F)
## Model 3 1.6258 0.12826 2.2069 0.001 ***
## Residual 45 11.0501 0.87174
## Total 48 12.6759 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## $parent_call
## [1] "filtered_counts_day1_wide_no0 ~ Region , strata = Null , permutations 999"
##
## $Canterbury_vs_Otago
## Df SumOfSqs R2 F Pr(>F)
## Model 1 0.4072 0.09027 1.786 0.045 *
## Residual 18 4.1038 0.90973
## Total 19 4.5110 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $Canterbury_vs_Marlborough
## Df SumOfSqs R2 F Pr(>F)
## Model 1 0.6737 0.07373 2.7063 0.01 **
## Residual 34 8.4640 0.92627
## Total 35 9.1378 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $Canterbury_vs_Wairarapa
## Df SumOfSqs R2 F Pr(>F)
## Model 1 0.5556 0.10107 2.3612 0.008 **
## Residual 21 4.9415 0.89893
## Total 22 5.4971 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $Otago_vs_Marlborough
## Df SumOfSqs R2 F Pr(>F)
## Model 1 0.3443 0.05336 1.3528 0.188
## Residual 24 6.1087 0.94664
## Total 25 6.4530 1.00000
##
## $Otago_vs_Wairarapa
## Df SumOfSqs R2 F Pr(>F)
## Model 1 0.42663 0.14161 1.8147 0.07 .
## Residual 11 2.58610 0.85839
## Total 12 3.01273 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $Marlborough_vs_Wairarapa
## Df SumOfSqs R2 F Pr(>F)
## Model 1 0.6893 0.09027 2.6792 0.01 **
## Residual 27 6.9463 0.90973
## Total 28 7.6356 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## attr(,"class")
## [1] "pwadstrata" "list"
## [1] "Analysing Carrot"
## Permutation test for adonis under reduced model
## Permutation: free
## Number of permutations: 999
##
## adonis2(formula = filtered_counts_day1_wide_no0 ~ Region, data = filtered_meta_data_day1_no0)
## Df SumOfSqs R2 F Pr(>F)
## Model 1 0.4344 0.0326 1.8535 0.03 *
## Residual 55 12.8901 0.9674
## Total 56 13.3245 1.0000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## $parent_call
## [1] "filtered_counts_day1_wide_no0 ~ Region , strata = Null , permutations 999"
##
## $Canterbury_vs_Otago
## Df SumOfSqs R2 F Pr(>F)
## Model 1 0.4344 0.0326 1.8535 0.04 *
## Residual 55 12.8901 0.9674
## Total 56 13.3245 1.0000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## attr(,"class")
## [1] "pwadstrata" "list"#####
#MDS#
#####
pk_NMDS <- metaMDS(counts_day1_wide_no0,trymax=500)## Square root transformation
## Wisconsin double standardization
## Run 0 stress 0.2378012
## Run 1 stress 0.2758738
## Run 2 stress 0.2388144
## Run 3 stress 0.2443292
## Run 4 stress 0.2390363
## Run 5 stress 0.2573102
## Run 6 stress 0.2474687
## Run 7 stress 0.2397428
## Run 8 stress 0.2378306
## ... Procrustes: rmse 0.001746814 max resid 0.01613573
## Run 9 stress 0.2404073
## Run 10 stress 0.2751305
## Run 11 stress 0.2408579
## Run 12 stress 0.2381609
## ... Procrustes: rmse 0.00445632 max resid 0.06699434
## Run 13 stress 0.2412538
## Run 14 stress 0.2380438
## ... Procrustes: rmse 0.00686162 max resid 0.06672309
## Run 15 stress 0.2421597
## Run 16 stress 0.2399348
## Run 17 stress 0.2381025
## ... Procrustes: rmse 0.009545383 max resid 0.1350442
## Run 18 stress 0.2776889
## Run 19 stress 0.2380405
## ... Procrustes: rmse 0.006833851 max resid 0.06673315
## Run 20 stress 0.2408359
## Run 21 stress 0.2790709
## Run 22 stress 0.2386308
## Run 23 stress 0.2448597
## Run 24 stress 0.2398169
## Run 25 stress 0.2385605
## Run 26 stress 0.2452983
## Run 27 stress 0.2394035
## Run 28 stress 0.238869
## Run 29 stress 0.2415471
## Run 30 stress 0.2428658
## Run 31 stress 0.240129
## Run 32 stress 0.2460718
## Run 33 stress 0.2381044
## ... Procrustes: rmse 0.005787396 max resid 0.05857116
## Run 34 stress 0.2652479
## Run 35 stress 0.239446
## Run 36 stress 0.2387149
## Run 37 stress 0.2400752
## Run 38 stress 0.2429264
## Run 39 stress 0.2403784
## Run 40 stress 0.2720263
## Run 41 stress 0.2387018
## Run 42 stress 0.2510884
## Run 43 stress 0.2382654
## ... Procrustes: rmse 0.01001398 max resid 0.1349484
## Run 44 stress 0.2391271
## Run 45 stress 0.2407738
## Run 46 stress 0.2399687
## Run 47 stress 0.2414134
## Run 48 stress 0.2400651
## Run 49 stress 0.2473941
## Run 50 stress 0.2383868
## Run 51 stress 0.2427548
## Run 52 stress 0.2377061
## ... New best solution
## ... Procrustes: rmse 0.005209985 max resid 0.05334213
## Run 53 stress 0.2378003
## ... Procrustes: rmse 0.003310026 max resid 0.04728152
## Run 54 stress 0.2390956
## Run 55 stress 0.2402813
## Run 56 stress 0.2380848
## ... Procrustes: rmse 0.01323656 max resid 0.1351793
## Run 57 stress 0.2399363
## Run 58 stress 0.2441241
## Run 59 stress 0.2381016
## ... Procrustes: rmse 0.01334711 max resid 0.1352235
## Run 60 stress 0.2390654
## Run 61 stress 0.2467184
## Run 62 stress 0.2401337
## Run 63 stress 0.2451346
## Run 64 stress 0.2414647
## Run 65 stress 0.2388181
## Run 66 stress 0.2422516
## Run 67 stress 0.2416757
## Run 68 stress 0.2439148
## Run 69 stress 0.2402291
## Run 70 stress 0.2475161
## Run 71 stress 0.2409379
## Run 72 stress 0.239157
## Run 73 stress 0.2390579
## Run 74 stress 0.2802639
## Run 75 stress 0.2391443
## Run 76 stress 0.2393274
## Run 77 stress 0.2431909
## Run 78 stress 0.2381016
## ... Procrustes: rmse 0.01371302 max resid 0.1352075
## Run 79 stress 0.2404269
## Run 80 stress 0.2409924
## Run 81 stress 0.2381048
## ... Procrustes: rmse 0.008657389 max resid 0.1176209
## Run 82 stress 0.2401405
## Run 83 stress 0.238101
## ... Procrustes: rmse 0.01337983 max resid 0.1352163
## Run 84 stress 0.2392874
## Run 85 stress 0.2412878
## Run 86 stress 0.238099
## ... Procrustes: rmse 0.008571599 max resid 0.1175787
## Run 87 stress 0.2814866
## Run 88 stress 0.238074
## ... Procrustes: rmse 0.01318344 max resid 0.1351341
## Run 89 stress 0.2413691
## Run 90 stress 0.2381447
## ... Procrustes: rmse 0.01055635 max resid 0.1351358
## Run 91 stress 0.2388232
## Run 92 stress 0.2380115
## ... Procrustes: rmse 0.007964465 max resid 0.117843
## Run 93 stress 0.2388289
## Run 94 stress 0.2377062
## ... Procrustes: rmse 0.000695841 max resid 0.005413691
## ... Similar to previous best
## *** Best solution repeated 1 times#converges - yay - a little stressed but is a lot of data
pk_NMDS##
## Call:
## metaMDS(comm = counts_day1_wide_no0, trymax = 500)
##
## global Multidimensional Scaling using monoMDS
##
## Data: wisconsin(sqrt(counts_day1_wide_no0))
## Distance: bray
##
## Dimensions: 2
## Stress: 0.2377061
## Stress type 1, weak ties
## Best solution was repeated 1 time in 94 tries
## The best solution was from try 52 (random start)
## Scaling: centring, PC rotation, halfchange scaling
## Species: expanded scores based on 'wisconsin(sqrt(counts_day1_wide_no0))'#stress
stressplot(pk_NMDS)
plot(pk_NMDS)
plot_df <- scores(pk_NMDS, display = "sites") %>%
as.data.frame() %>%
rownames_to_column("site") %>%
full_join(meta_data_day1_no0, by = c("site" = "sample_ID"))
#plot_df$Crop <- factor(plot_df$Crop, levels = c("Apple","Pear","Avocado","Kiwifruit","Pak Choi","Carrot", "Onion","Radish"))
plot_df$Crop <- factor(plot_df$Crop, levels = c("Avocado","Apple","Pear","Kiwifruit","Pak Choi","Radish", "Carrot","Onion"))
plot_nmds <- ggplot(plot_df, aes(x = NMDS1, y = NMDS2, color = Crop)) +
geom_point(size = 2, alpha = 0.8) +
stat_ellipse(linetype = 2, linewidth = 0.5) +
theme_bw()+
scale_colour_manual(values=col_bound)+
labs(title = "NMDS")
plot_nmds
plot_nmds <- ggplot(plot_df, aes(x = NMDS1, y = NMDS2, color = Crop)) +
geom_point(size = 2, alpha = 1) +
stat_ellipse(linetype = 2, linewidth = 0.5) +
theme_bw()+
scale_colour_manual(values=col_bound)+
labs(title = "NMDS")
plot_nmds
plot_nmds <- ggplot(plot_df, aes(x = NMDS1, y = NMDS2, color = Crop,shape=Crop)) +
geom_point(size = 3, alpha = 1) +
scale_shape_manual(values = c("Avocado" = 15, "Apple" = 15, "Pear" = 15, "Kiwifruit" = 15, "Pak Choi" = 16, "Radish" = 16, "Carrot" = 16, "Onion" = 16))+
stat_ellipse(linetype = 2, linewidth = 0.5) +
theme_bw()+
scale_colour_manual(values=col_bound)+
labs(title = "NMDS")
plot_nmds
plot_nmds <- ggplot(plot_df, aes(x = NMDS1, y = NMDS2, color = Crop,shape=Crop)) +
geom_point(size = 2, alpha = 1) +
scale_shape_manual(values = c("Avocado" = 16, "Apple" = 16, "Pear" = 16, "Kiwifruit" = 16, "Pak Choi" = 15, "Radish" = 15, "Carrot" = 15, "Onion" = 15))+
stat_ellipse(linetype = 1, linewidth = 0.5) +
theme_bw()+
scale_colour_manual(values=col_bound)+
labs(title = "NMDS")
plot_nmds
#apple makes that look a bit dumb
#its only got a few samples which is why its so biased to that outlier. I'd just delete the cirle for apple on the previous as i think its too dodgy -
plot_nmds <- ggplot(plot_df, aes(x = NMDS1, y = NMDS2, color = Crop,shape=Crop)) +
geom_point(size = 2, alpha = 1) +
scale_shape_manual(values = c("Avocado" = 16, "Apple" = 16, "Pear" = 16, "Kiwifruit" = 16, "Pak Choi" = 15, "Radish" = 15, "Carrot" = 15, "Onion" = 15))+
stat_ellipse(linewidth = 0.5,aes(linetype=Crop)) +
scale_linetype_manual(values = c(1,0,1,1,1,1,1,1)) +
theme_bw()+
scale_colour_manual(values=col_bound)+
labs(title = "NMDS")
plot_nmds
# If you set the min at 6 it drops that outlier and wont draw a circle as there is to few points for apple
counts_day1_wide_no0 <- counts_day1_wide[rowSums(counts_day1_wide) >= 6, ]
#remove things that are only from a single species
counts_day1_wide_no0 <-counts_day1_wide_no0[which(rowSums(1*(counts_day1_wide_no0!=0))>1),]
meta_data_day1_no0<-meta_data_day1 %>% filter(sample_ID %in% row.names(counts_day1_wide_no0))
#MDS
pk_NMDS <- metaMDS(counts_day1_wide_no0,trymax=200)## Square root transformation
## Wisconsin double standardization
## Run 0 stress 0.2376334
## Run 1 stress 0.2384927
## Run 2 stress 0.2424308
## Run 3 stress 0.2419076
## Run 4 stress 0.2389921
## Run 5 stress 0.2427326
## Run 6 stress 0.239079
## Run 7 stress 0.2730656
## Run 8 stress 0.2448312
## Run 9 stress 0.2390658
## Run 10 stress 0.2376368
## ... Procrustes: rmse 0.007898849 max resid 0.0696041
## Run 11 stress 0.2435468
## Run 12 stress 0.2456612
## Run 13 stress 0.2403293
## Run 14 stress 0.2712284
## Run 15 stress 0.2384928
## Run 16 stress 0.2410051
## Run 17 stress 0.2446873
## Run 18 stress 0.2412981
## Run 19 stress 0.2483773
## Run 20 stress 0.2440246
## Run 21 stress 0.2717166
## Run 22 stress 0.240338
## Run 23 stress 0.2402729
## Run 24 stress 0.2378641
## ... Procrustes: rmse 0.01027951 max resid 0.1227496
## Run 25 stress 0.2424577
## Run 26 stress 0.2399013
## Run 27 stress 0.2397159
## Run 28 stress 0.2409032
## Run 29 stress 0.2424961
## Run 30 stress 0.2376594
## ... Procrustes: rmse 0.006591371 max resid 0.0697555
## Run 31 stress 0.2397419
## Run 32 stress 0.2375283
## ... New best solution
## ... Procrustes: rmse 0.002532341 max resid 0.03293485
## Run 33 stress 0.2454572
## Run 34 stress 0.2384927
## Run 35 stress 0.2468134
## Run 36 stress 0.2377779
## ... Procrustes: rmse 0.00735594 max resid 0.0700589
## Run 37 stress 0.2743363
## Run 38 stress 0.269266
## Run 39 stress 0.2427951
## Run 40 stress 0.2422429
## Run 41 stress 0.2773211
## Run 42 stress 0.2407808
## Run 43 stress 0.2380439
## Run 44 stress 0.2402332
## Run 45 stress 0.2373975
## ... New best solution
## ... Procrustes: rmse 0.00562685 max resid 0.056937
## Run 46 stress 0.2522656
## Run 47 stress 0.2613746
## Run 48 stress 0.242316
## Run 49 stress 0.2377278
## ... Procrustes: rmse 0.01275454 max resid 0.1426887
## Run 50 stress 0.286028
## Run 51 stress 0.2378456
## ... Procrustes: rmse 0.00875887 max resid 0.07006082
## Run 52 stress 0.2409389
## Run 53 stress 0.2436376
## Run 54 stress 0.250289
## Run 55 stress 0.2432705
## Run 56 stress 0.2473091
## Run 57 stress 0.2761395
## Run 58 stress 0.2379687
## Run 59 stress 0.2409205
## Run 60 stress 0.2381663
## Run 61 stress 0.238075
## Run 62 stress 0.259727
## Run 63 stress 0.2412031
## Run 64 stress 0.2407882
## Run 65 stress 0.2414526
## Run 66 stress 0.2449933
## Run 67 stress 0.2412247
## Run 68 stress 0.2376066
## ... Procrustes: rmse 0.007444668 max resid 0.06516947
## Run 69 stress 0.2438985
## Run 70 stress 0.2570145
## Run 71 stress 0.248685
## Run 72 stress 0.2412298
## Run 73 stress 0.2390522
## Run 74 stress 0.2440053
## Run 75 stress 0.2592219
## Run 76 stress 0.2403225
## Run 77 stress 0.2433894
## Run 78 stress 0.2602514
## Run 79 stress 0.2775988
## Run 80 stress 0.2433927
## Run 81 stress 0.2404266
## Run 82 stress 0.2450206
## Run 83 stress 0.2402336
## Run 84 stress 0.2381677
## Run 85 stress 0.274583
## Run 86 stress 0.2488718
## Run 87 stress 0.2424638
## Run 88 stress 0.2397421
## Run 89 stress 0.2413943
## Run 90 stress 0.2445654
## Run 91 stress 0.2459383
## Run 92 stress 0.2378735
## ... Procrustes: rmse 0.00825596 max resid 0.1233702
## Run 93 stress 0.2398114
## Run 94 stress 0.2381736
## Run 95 stress 0.2391284
## Run 96 stress 0.2454567
## Run 97 stress 0.2419211
## Run 98 stress 0.2384921
## Run 99 stress 0.2404427
## Run 100 stress 0.2375183
## ... Procrustes: rmse 0.005719828 max resid 0.05737251
## Run 101 stress 0.2378539
## ... Procrustes: rmse 0.008715055 max resid 0.07007264
## Run 102 stress 0.2432587
## Run 103 stress 0.2409844
## Run 104 stress 0.2401132
## Run 105 stress 0.2559711
## Run 106 stress 0.2449837
## Run 107 stress 0.2388654
## Run 108 stress 0.2378549
## ... Procrustes: rmse 0.008293612 max resid 0.1232403
## Run 109 stress 0.2376227
## ... Procrustes: rmse 0.006205019 max resid 0.05710754
## Run 110 stress 0.2403512
## Run 111 stress 0.246265
## Run 112 stress 0.2440402
## Run 113 stress 0.2460693
## Run 114 stress 0.2401189
## Run 115 stress 0.241233
## Run 116 stress 0.2440839
## Run 117 stress 0.2493573
## Run 118 stress 0.2424261
## Run 119 stress 0.2656262
## Run 120 stress 0.2378178
## ... Procrustes: rmse 0.01210936 max resid 0.1423696
## Run 121 stress 0.2420289
## Run 122 stress 0.280029
## Run 123 stress 0.2376603
## ... Procrustes: rmse 0.006022004 max resid 0.0700955
## Run 124 stress 0.2749764
## Run 125 stress 0.2376069
## ... Procrustes: rmse 0.004803886 max resid 0.07017056
## Run 126 stress 0.2419189
## Run 127 stress 0.2409292
## Run 128 stress 0.2462666
## Run 129 stress 0.2375268
## ... Procrustes: rmse 0.005619069 max resid 0.0569673
## Run 130 stress 0.2375829
## ... Procrustes: rmse 0.005729844 max resid 0.05688859
## Run 131 stress 0.2525758
## Run 132 stress 0.2378003
## ... Procrustes: rmse 0.01210243 max resid 0.1422983
## Run 133 stress 0.2629855
## Run 134 stress 0.250366
## Run 135 stress 0.2379684
## Run 136 stress 0.2402339
## Run 137 stress 0.2432122
## Run 138 stress 0.2376084
## ... Procrustes: rmse 0.007451546 max resid 0.06523173
## Run 139 stress 0.2409942
## Run 140 stress 0.2375208
## ... Procrustes: rmse 0.005734811 max resid 0.05720373
## Run 141 stress 0.2388495
## Run 142 stress 0.2375404
## ... Procrustes: rmse 0.005687434 max resid 0.05751529
## Run 143 stress 0.2376063
## ... Procrustes: rmse 0.00479671 max resid 0.07014551
## Run 144 stress 0.2375782
## ... Procrustes: rmse 0.00350692 max resid 0.05172213
## Run 145 stress 0.2419136
## Run 146 stress 0.2433711
## Run 147 stress 0.2423058
## Run 148 stress 0.2766062
## Run 149 stress 0.2378447
## ... Procrustes: rmse 0.00876261 max resid 0.0700771
## Run 150 stress 0.2374034
## ... Procrustes: rmse 0.001738193 max resid 0.01406857
## Run 151 stress 0.2376451
## ... Procrustes: rmse 0.004905909 max resid 0.07021101
## Run 152 stress 0.2505707
## Run 153 stress 0.2433926
## Run 154 stress 0.2393344
## Run 155 stress 0.2411317
## Run 156 stress 0.237773
## ... Procrustes: rmse 0.01125971 max resid 0.1423973
## Run 157 stress 0.2454501
## Run 158 stress 0.2375523
## ... Procrustes: rmse 0.005837982 max resid 0.05698495
## Run 159 stress 0.2506548
## Run 160 stress 0.2402782
## Run 161 stress 0.2379686
## Run 162 stress 0.241129
## Run 163 stress 0.239379
## Run 164 stress 0.2402556
## Run 165 stress 0.2384928
## Run 166 stress 0.2400587
## Run 167 stress 0.2438917
## Run 168 stress 0.2749806
## Run 169 stress 0.2376431
## ... Procrustes: rmse 0.009511241 max resid 0.1428278
## Run 170 stress 0.2502348
## Run 171 stress 0.237855
## ... Procrustes: rmse 0.008293446 max resid 0.1232296
## Run 172 stress 0.240071
## Run 173 stress 0.2704945
## Run 174 stress 0.2402241
## Run 175 stress 0.2432453
## Run 176 stress 0.2401896
## Run 177 stress 0.2400808
## Run 178 stress 0.2548766
## Run 179 stress 0.2407476
## Run 180 stress 0.2403575
## Run 181 stress 0.237371
## ... New best solution
## ... Procrustes: rmse 0.001189537 max resid 0.01216646
## Run 182 stress 0.2411862
## Run 183 stress 0.2417426
## Run 184 stress 0.2415278
## Run 185 stress 0.2736192
## Run 186 stress 0.2595163
## Run 187 stress 0.2611282
## Run 188 stress 0.2618645
## Run 189 stress 0.242585
## Run 190 stress 0.2385059
## Run 191 stress 0.2867988
## Run 192 stress 0.2382101
## Run 193 stress 0.2386222
## Run 194 stress 0.2397418
## Run 195 stress 0.2399745
## Run 196 stress 0.258668
## Run 197 stress 0.2480442
## Run 198 stress 0.2789674
## Run 199 stress 0.2573066
## Run 200 stress 0.2499681
## *** Best solution was not repeated -- monoMDS stopping criteria:
## 27: no. of iterations >= maxit
## 168: stress ratio > sratmax
## 5: scale factor of the gradient < sfgrmin#converges - yay - a little stressed but is a lot of data
pk_NMDS##
## Call:
## metaMDS(comm = counts_day1_wide_no0, trymax = 200)
##
## global Multidimensional Scaling using monoMDS
##
## Data: wisconsin(sqrt(counts_day1_wide_no0))
## Distance: bray
##
## Dimensions: 2
## Stress: 0.237371
## Stress type 1, weak ties
## Best solution was not repeated after 200 tries
## The best solution was from try 181 (random start)
## Scaling: centring, PC rotation, halfchange scaling
## Species: expanded scores based on 'wisconsin(sqrt(counts_day1_wide_no0))'#stress
stressplot(pk_NMDS)
plot(pk_NMDS)
plot_df <- scores(pk_NMDS, display = "sites") %>%
as.data.frame() %>%
rownames_to_column("site") %>%
full_join(meta_data_day1_no0, by = c("site" = "sample_ID"))
#plot_df$Crop <- factor(plot_df$Crop, levels = c("Apple","Pear","Avocado","Kiwifruit","Pak Choi","Carrot", "Onion","Radish"))
plot_df$Crop <- factor(plot_df$Crop, levels = c("Avocado","Apple","Pear","Kiwifruit","Pak Choi","Radish", "Carrot","Onion"))
plot_nmds <- ggplot(plot_df, aes(x = NMDS1, y = NMDS2, color = Crop)) +
geom_point(size = 2, alpha = 0.8) +
stat_ellipse(linetype = 2, linewidth = 0.5) +
theme_bw()+
scale_colour_manual(values=col_bound)+
labs(title = "NMDS")
plot_nmds## Too few points to calculate an ellipse## Warning: Removed 1 row containing missing values or values outside the scale range
## (`geom_path()`).
plot_nmds <- ggplot(plot_df, aes(x = NMDS1, y = NMDS2, color = Crop)) +
geom_point(size = 2, alpha = 1) +
stat_ellipse(linetype = 2, linewidth = 0.5) +
theme_bw()+
scale_colour_manual(values=col_bound)+
labs(title = "NMDS")
plot_nmds## Too few points to calculate an ellipse## Warning: Removed 1 row containing missing values or values outside the scale range
## (`geom_path()`).
plot_nmds <- ggplot(plot_df, aes(x = NMDS1, y = NMDS2, color = Crop,shape=Crop)) +
geom_point(size = 3, alpha = 1) +
scale_shape_manual(values = c("Avocado" = 15, "Apple" = 15, "Pear" = 15, "Kiwifruit" = 15, "Pak Choi" = 16, "Radish" = 16, "Carrot" = 16, "Onion" = 16))+
stat_ellipse(linetype = 2, linewidth = 0.5) +
theme_bw()+
scale_colour_manual(values=col_bound)+
labs(title = "NMDS")
plot_nmds## Too few points to calculate an ellipse## Warning: Removed 1 row containing missing values or values outside the scale range
## (`geom_path()`).
plot_nmds <- ggplot(plot_df, aes(x = NMDS1, y = NMDS2, color = Crop,shape=Crop)) +
geom_point(size = 2, alpha = 1) +
scale_shape_manual(values = c("Avocado" = 16, "Apple" = 16, "Pear" = 16, "Kiwifruit" = 16, "Pak Choi" = 15, "Radish" = 15, "Carrot" = 15, "Onion" = 15))+
stat_ellipse(linetype = 1, linewidth = 0.5) +
theme_bw()+
scale_colour_manual(values=col_bound)+
labs(title = "NMDS")
plot_nmds## Too few points to calculate an ellipse## Warning: Removed 1 row containing missing values or values outside the scale range
## (`geom_path()`).
##innext day 1
########
#innext#
########
#innext by its nature should be robust for sampling bias but just for evenness sake Im rerunning on day 1 samples alone
library(iNEXT.3D)
#generate a list of the unique sample locations
categories <- unique(meta_data$Crop)
categories## [1] "Apple" "Pear" "Avocado" "Kiwifruit" "Pak Choi" "Radish"
## [7] "Onion" "Carrot"#create empty list
split_list <- list()
#fill empty list with a presence absence OTU from each location
head(allcrops_per_r_fly)## # A tibble: 6 × 7
## sample_ID Ordering Region `Location and season` Day Crop raw_count
## <chr> <fct> <chr> <chr> <chr> <chr> <dbl>
## 1 Apple_Hawkes Bay_… 32 Hawke… 104 Moteo-Pa road _2… Day_1 Apple 2
## 2 Apple_Hawkes Bay_… 33 Hawke… 104 Moteo-Pa road _2… Day_1 Apple 1
## 3 Apple_Hawkes Bay_… 34 Hawke… 104 Moteo-Pa road _2… Day_1 Apple 11
## 4 Apple_Hawkes Bay_… 54 Hawke… 104 Moteo-Pa road _2… Day_1 Apple 1
## 5 Apple_Hawkes Bay_… 34 Hawke… 104 Moteo-Pa road _2… Day_3 Apple 11
## 6 Apple_Hawkes Bay_… 14 Hawke… 185 Lawn road, Hasti… Day_1 Apple 1#I need to fill up the NA's for the samples with 0 flies with some dummy 0 for ones species to keep them in the next stage - so this is just to add a 0 value in and I've chosed the insect '27' at random (cause i know it occurs in the dataset so Im not adding something not present)
#in '' as its a factor not a number
#filter to day 1 only
allcrops_per_r_fly_innext<-allcrops_per_r_fly %>% filter(Day=='Day_1')
allcrops_per_r_fly_innext$Ordering[is.na(allcrops_per_r_fly_innext$Ordering)] <- '27'
allcrops_per_r_fly_innext$raw_count[is.na(allcrops_per_r_fly_innext$raw_count)] <- 0
#create empty list
split_list <- list()
for (category in categories) {
print(category)
subset_data <- allcrops_per_r_fly_innext %>% filter(Crop == category)
# subset_data <- allcrops_per_r_fly_innext %>% filter(Crop == "Carrot")
test<-subset_data %>% ungroup() %>% dplyr::select(sample_ID,Ordering,raw_count)
test <- test %>% spread(key = sample_ID, value = raw_count)
test<-test %>% remove_rownames %>% column_to_rownames(var="Ordering")
test[is.na(test)] <- 0
# test <- test[rowSums(test == 0) != ncol(test), ]
test<-test %>% mutate(across(everything(), ~ ifelse(. > 0, 1, 0)))
split_list[[category]]<-as(test,'matrix')
} ## [1] "Apple"
## [1] "Pear"
## [1] "Avocado"
## [1] "Kiwifruit"
## [1] "Pak Choi"
## [1] "Radish"
## [1] "Onion"
## [1] "Carrot"matrix_list <- list(data = list())
for (category in categories) {
otu_table <- as(split_list[[category]], "matrix")
matrix_list[["data"]][[category]] <- otu_table
}
#here Im setting the endpoint as NULL which is double the input sample size (this is probably most robust)
out.raw <- iNEXT3D(data = matrix_list$data, diversity = 'TD', q = c(0, 1, 2), datatype = 'incidence_raw', nboot = 50,endpoint=NULL)
#there is various ways to look at these plots:
#within a crop
ggiNEXT3D(out.raw, type = 1, facet.var = 'Assemblage') + facet_wrap(~Assemblage, nrow = 3)
#across crops
ggiNEXT3D(out.raw, type = 1, facet.var = "Order.q")## Warning: The shape palette can deal with a maximum of 6 discrete values because more
## than 6 becomes difficult to discriminate
## ℹ you have requested 8 values. Consider specifying shapes manually if you need
## that many of them.## Warning: Removed 6 rows containing missing values or values outside the scale range
## (`geom_point()`).
#sample completeness
ggiNEXT3D(out.raw, type = 2, facet.var = "Order.q", color.var = "Assemblage")## Warning: The shape palette can deal with a maximum of 6 discrete values because more
## than 6 becomes difficult to discriminate
## ℹ you have requested 8 values. Consider specifying shapes manually if you need
## that many of them.## Warning: Removed 2 rows containing missing values or values outside the scale range
## (`geom_point()`).
#running innext as abundance as well
#create empty list
split_list <- list()
for (category in categories) {
print(category)
subset_data <- allcrops_per_r_fly_innext %>% filter(Crop == category)
# subset_data <- allcrops_per_r_fly_innext %>% filter(Crop == "Carrot")
test<-subset_data %>% ungroup() %>% dplyr::select(sample_ID,Ordering,raw_count)
test <- test %>% spread(key = sample_ID, value = raw_count)
test<-test %>% remove_rownames %>% column_to_rownames(var="Ordering")
test[is.na(test)] <- 0
test<-as.data.frame(rowSums(test))
colnames(test) <- c(paste0(category))
# test <- test[rowSums(test == 0) != ncol(test), ]
# test<-test %>% mutate(across(everything(), ~ ifelse(. > 0, 1, 0)))
split_list[[category]]<-as(test,'matrix')
} ## [1] "Apple"
## [1] "Pear"
## [1] "Avocado"
## [1] "Kiwifruit"
## [1] "Pak Choi"
## [1] "Radish"
## [1] "Onion"
## [1] "Carrot"matrix_list <- list(data = list())
for (category in categories) {
otu_table <- as(split_list[[category]], "matrix")
matrix_list[["data"]][[category]] <- otu_table
}
#here Im setting the endpoint as NULL which is double the input sample size (this is probably most robust)
out.raw <- iNEXT3D(data = matrix_list$data, diversity = 'TD', q = c(0, 1, 2), datatype = 'abundance', nboot = 50,endpoint=NULL)
#there is various ways to look at these plots:
#within a sample
ggiNEXT3D(out.raw, type = 1, facet.var = 'Assemblage') + facet_wrap(~Assemblage, nrow = 3)
#across samples
ggiNEXT3D(out.raw, type = 1, facet.var = "Order.q")## Warning: The shape palette can deal with a maximum of 6 discrete values because more
## than 6 becomes difficult to discriminate
## ℹ you have requested 8 values. Consider specifying shapes manually if you need
## that many of them.## Warning: Removed 6 rows containing missing values or values outside the scale range
## (`geom_point()`).